I created an OpenGL/OpenGLES based inference framework a while back which is rather GPU-agnostic and might be a good option for distributing multi-platform ML solutions for platforms ranging from Android over desktop to WebGL(2). Quite recently I added support for LLMs to that (restricted to 4-bit quantized Llama models for now). The LLM-enabled fork can be found here (compileable sample code inside). Maybe someone finds this useful. Also looking for collaborators to extend the functionality. ​ submitted by /u/mtnwrw [link] [comments]  ( 9 min )

I’m looking for subreddits and resources in general for Ai alignment. I recently read Life 3.0 by Max Tegmark and The Alignment Problem by Brian Christian. I was unaware so much was going on in the space. I am CEO of an AI startup, we have a compassionate AI. Most of the alignment focus out there is on superintelligence, little out there focuses on building modern day Ai that benefits humans. That’s been my focus for 17 years when I dreamed up what was possible and sent a proposal to executives at the mfaang corporation I was working at. I found out back then business doesn’t give a rats ass about Ai that actively makes its users life’s better. Well now I have proof that customers care. I’m hoping to find some resources on Ai alignment groups, conferences, more books etc. I’ll need to hire skilled ml engineers who do give a rats ass. Compassionate Ai isn’t just feel good tech. It would be good to do some networking. AI alignment doesn’t have to wait for AGI and super intelligence. It can and should be a focus today. My startup is proving it. Hoping to find a conference to network and share our research. submitted by /u/xyz_TrashMan_zyx [link] [comments]  ( 9 min )

Hi guys , TorchClassifierData is A small pytorch utility to Import, Split ,Normalize and Visualize custom dataset for classification tasks. wich is indispensable for real word problem . You can find a full notebook that use TorchClassifierData to train a classifier on this kaggle dataset here. The code source is avalaible on my github. Thank you. submitted by /u/charles_data_dev [link] [comments]  ( 9 min )

Diffusion models are basically Langevin sampling. What are the key differences and tricks that set them apart from Langevin dynamics? Why do they work so well while very similar sampling methods don't? submitted by /u/Dangerous-Flan-6581 [link] [comments]  ( 8 min )

​ https://preview.redd.it/cczbhu367aqb1.png?width=1600&format=png&auto=webp&s=f1761911d7ce3bbefaef43774b5d60f638886893 ML is often portrayed as a magical field where anyone with a laptop and Python skills can build amazing AI systems. The reality is less democratic: mastering ML requires gritty, systematic work best learned through formal training. You need rock solid foundations in math, programming, and core concepts—skills acquired through advanced education, which (almost always) is beyond self-taught hackers. Most think a PhD is unnecessary, but the reality is that advanced degrees provide the deepest training. Patience and persistence do matter, but a PhD gives you a 10-year head start over weekend warriors. The hype overlooks the work and education needed to excel. This article has a great blueprint of all the required skills to become a ML Engineer (in the CV field). submitted by /u/btcmx [link] [comments]  ( 9 min )

So briefly about my current experience, I graduated 2 years ago with a bachelor in data science and I have 2-3 years of experience as a data scientist/ml engineer/software engineer. So I’ve got competing offers, one from the big 4 accounting firms as a software systems engineer - AI/ML (Big 4) and the other as a machine learning engineer. The startup salary is higher while big 4 is lower. Additionally the startup isn’t necessarily a unicorn it’s a relatively small startup with an interesting product but it doesn’t necessarily blow me away. The salary at the startup is 15 percent higher that that of the big 4 offer. For those wondering I did already negotiate the salary and they did increase it marginally. I am conflicted because I think that the big 4 jobs will have 1) more career growth 2) more potential future opportunities and 3) more networking potential Is this an accurate assessment? Which is the best job to take for maximum future potential? submitted by /u/zorenum [link] [comments]  ( 9 min )

So I've been thinking of implementing SwinBert for a college project and have been wondering what all resources i would be needing for a computer. Any ideas? submitted by /u/Big-Brain_69 [link] [comments]  ( 8 min )

I’ve been having a bit of an existential crisis as of late and wanted to ask for advice on how to move forward. I have a Master’s in CS with research experience and a few publications applying machine learning in a fairly niche area (So not novel from the ML side). Since graduating, I’ve worked ~2 years as an ML engineer in small company(Niche area, different than research). I’ve done quite well here and have played a critical role in taking several big greenfield projects to completion. Most of my work is framing problems, understanding what’s possible with current research, then building the data pipelines, and training models(with small mods here and there). My main worry is that I might be approaching a point where there won’t be any more problems I’m capable of solving here. I’d imagine I’d hit the same wall at any future company with my current skill set. I’d like to continue working in CV/Graphics/Perception if possible, but I’d also like to be realistic about the competitiveness of this particular subfield and my general ability. I’ve been trying to up-skill and am struggling to self study MVG by Hartley and Zisserman. I’m also looking into OMCS to review low-level programming - maybe I can transition to optimizing ml/cv algorithms? It hit me recently that I don’t really know what to study/what I’d benefit from learning. Hence this post. Any advice would be most appreciated! submitted by /u/answersareallyouneed [link] [comments]  ( 9 min )

I'm curious if anyone has tried pix2struct-large for web-scraping text from wesites. If so - how well did it perform? If not - is there something else that is considered better to use? submitted by /u/ReddSpark [link] [comments]  ( 8 min )

Hypothesis: Big tech companies are investing vast amounts of money to develop general models on which others will build. They'll develop interfaces to make it easier for others to fine-tune on top of their models. So that there will be less and less of a need for ML engineers that know how to create a deep learning model in Pytorch, and more and more of a need for data engineers that simply plug into pre-trained models. An AI assistant will also be quicker at coding up a more bespoke AI model for a companies needs, guided by data engineers. What do people think? Is this a scenario that they think will play out? Where will the demand for AI skills be coming from in the future? submitted by /u/QuintBa [link] [comments]  ( 9 min )

LLMs are great with words but can't handle visual tasks like understanding images. Teaching them to use visual tools could make them much more capable. A new paper introduces GPT4Tools - a method to efficiently teach existing LLMs to invoke tools for visual tasks without proprietary data. My highlights from the paper: Uses ChatGPT as a "teacher" to generate instructional data for other LLMs Fine-tunes LLMs like Vicuna on this data using selective weight tuning (keeps base model frozen) Allows smaller 13B LLM to match 175B GPT-3.5 on seen tools after tuning Data augmentation with negative/context samples was found to be the secret sauce to get this to work Can generalize to brand new visual tools in a zero-shot way This is big because it shows we may not need hyper-expensive training of massive models to impart visual capabilities to LLMs. They seems to be generalizable enough that they can be taught to work with images. Some examples shown include counting objects or segmenting items in pictures using other tools. With this approach, existing models can be made multi-modal! Pretty cool. Full summary. Original paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

Hey all, I run a small team & we are collaborating on a few data sets that we use to fine-tune GPT3.5, We are currently using Google Sheets and I'm wondering if there is a tool where we can organize our data preferably with version control Any ideas? submitted by /u/zeJaeger [link] [comments]  ( 9 min )

Hello, I have created point clouds from images using Point-E. Sadly they are very sparse (for example wehn inputting an image of a house, the roof has very few points in it) and I was searching for other Models, that could make the PC more dense and predict the color of every point. Point-E outputs xyz and rgb vectors for every point. Do some of you have advise for me here? submitted by /u/bySmily [link] [comments]  ( 9 min )

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Hi I'm a student and working on a RL project for the university and need some guidance. I have created a 3d model with mujoco (I have the xml file) how do I create an environment in gymnasium with this xml file? for the sake of an example let's say I have the xml file of the humanoid model how do I load this in gymnasium so that I could train it to walk? (this is just an example because the current project is harder to explain, but will use the humanoid model in the project) or is the approach that I'm trying is not appropriate at all? I came across this stackoverflow post where they say mujoco is itself good for this but was hard for me to understand due to lack of examples. would really appreciate some advice and guidance thank you. submitted by /u/rakk109 [link] [comments]  ( 9 min )

I just open-sourced (MIT License) semantic segmentation annotation tool powered by segment-anything model that I used for a while in my projects. Hopefully it will help someone as it seems to me that it is more suitable for small projects than popular huge web based annotation tools. Link to the project: SAMAT (any feedback in Discussions section on GitHub is appreciated) Features: Brush annotation (opposed to polygons) Magic Wand (like in Photoshop) powered by segment-anything masks (it is optional, if you don’t have cool GPU to prepare masks) samat showcase Why yet another annotation tool? Before starting this project I tried supervisely, segments.ai, roboflow and several others, but found them not convenient for my tasks. Their cons, I tried to fix with my tool: Latency: they are web based, hence burden with irritating latency during labeling (SAMAT is snappy because it is a local desktop app) Complicated: too much features, hence overloaded UI (SAMAT is just a colored brush) P.S. there is another labeling tool called SALT on github which also uses segment-anything model, but it follows different approach to UI/UX, may be it will be more suitable for you, so take a look at it too. submitted by /u/Divelix [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2309.11672 Abstract: In this research, we explore the efficacy and potential of Generative AI models, specifically focusing on their application in role-playing simulations exemplified through Spyfall, a renowned mafia-style game. By leveraging GPT-4's advanced capabilities, the study aimed to showcase the model's potential in understanding, decision-making, and interaction during game scenarios. Comparative analyses between GPT-4 and its predecessor, GPT-3.5-turbo, demonstrated GPT-4's enhanced adaptability to the game environment, with significant improvements in posing relevant questions and forming human-like responses. However, challenges such as the model;s limitations in bluffing and predicting opponent moves emerged. Reflections on game development, fi…  ( 9 min )

Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]  ( 9 min )

For those who have tried Microsoft's Arsenal tool in conjunction with MITRE's offerings, how does it compare to other AI security tools you've used? submitted by /u/Agile_Temperature678 [link] [comments]  ( 8 min )

so I've had this problem where I try to train the model in RVC but the training stops after: INFO:torch.nn.parallel.distributed:Reducer buckets have been rebuilt in this iteration. Does anyone know why is it happening and how can I fix it? this is what it shows to me and idk what to do... any help would be appreciated thank you submitted by /u/mannequin7412 [link] [comments]  ( 9 min )

Our company has a new data science team and the team is relatively in experienced. I am working on a regression ML project and want to deploy it using best practices.What materials are there for learning how to implement CI/CD pipelines that deal with data transformation/model building/testing/deploying? The company uses azure environment with databricks/azure devops setup. I appreciate resources that show examples on how to setup MLOps on the company's environments. submitted by /u/userid95 [link] [comments]  ( 9 min )

[D] I watch this MIT lecture and I don't know if my understanding of wx + b = 0 is correct or not. Every explanation of the hyperplane state that all the points which are orthogonal to the vector w are in the hyperplane. However, all data point coordinates are defined wrt origin. So in order to attain this objective, we define the hyperplane as : wx = c This vector w is the vector which is normal to the hyperplane. And the data points x when dot producting with w outputting c are the points which are in the hyperplane due to the coordinates representation of x wrt origin. So points in the hyperplane are the points which when performing dot product with w equals 0 when the coordinates is defined wrt origin that lie in the hyperplane is equivalent to the points which when performing dot product with w equals c when the coordinates is defined wrt original origin (0) or (0,0) or ... submitted by /u/Emotional-Fox-4285 [link] [comments]  ( 9 min )

Hi, I have a fairly good understanding of how LLMs work under the hood, the attention mechanism, the different architectures and so on. However most of that knowledge takes the backseat in practical cases, especially in the industry. Are there any resources which discuss practical LLM engineering and the challenges that come with it? I'm talking about everything from fine-tuning to dealing with tokenisation limit to optimising the vectorDB and so on? submitted by /u/thoraway0612 [link] [comments]  ( 9 min )

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Hello to everyone. I'm lookin for some good AI github project to convert the language spoken in a video to a different language,like heygen / labs / video translate,but free. I mean,I can't afford prices like those and I think a few of us can,but I'm sure that the developers at heygen took some project born and hosted on some github and they have improved it. Would someone share some of those github projects ? thanks. submitted by /u/loziomario [link] [comments]  ( 9 min )

Hi there. This is a wonderful sub, and it's hard to please everyone on SUCH a broad topic... but it's intriguing to think about, as a lifelong magazine fan (and websites of course) - about using AI to filter, craft and create an amazing website/magazine ABOUT AI - well-categorized, maybe also human/editor curated/tweaked, but as fresh as ... well, the artificial sub but out of the Reddit bold/design into something ... well, something I'd read, visit a lot and love to help make work as a writer/editor (on the side of my "real" all-consuming gig of course;-) After all, if AI can 'make great code'/create great websites, maybe some folks out there are already trying to make this happen, for ease of information availability and organization. I can picture the departments/sections/categories now - I bet you can too! Anyone gone very far down that road yet (maybe the folks already doing the PC Magazines of the world) of organizing the vast fast-moving info beyond the AI Brews, Ben Parr's AI Analyst, etc.? Fun or a life-long journalist and tech geek (but not a coder, gamer etc.) to think about, at least! submitted by /u/barneylerten [link] [comments]  ( 9 min )

LLMs are great with words but can't handle visual tasks like understanding images. Teaching them to use visual tools could make them much more capable. A new paper introduces GPT4Tools - a method to efficiently teach existing LLMs to invoke tools for visual tasks without proprietary data. My highlights from the paper: Uses ChatGPT as a "teacher" to generate instructional data for other LLMs Fine-tunes LLMs like Vicuna on this data using selective weight tuning (keeps base model frozen) Allows smaller 13B LLM to match 175B GPT-3.5 on seen tools after tuning Data augmentation with negative/context samples was found to be the secret sauce to get this to work Can generalize to brand new visual tools in a zero-shot way This is big because it shows we may not need hyper-expensive training of massive models to impart visual capabilities to LLMs. They seem to be generalizable enough that they can be taught to work with images. Some examples shown include counting objects or segmenting items in pictures using other tools. With this approach, existing models can be made multi-modal! Pretty cool. Full summary. Original paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

A recent paper challenges assumptions about the energy use of AI models, finding that AI systems emit significantly fewer carbon dioxide equivalents (CO2e) compared to humans when producing text or images. The authors emphasize the importance of measuring carbon emissions from AI activities to inform sustainability policies. The ongoing debate among AI researchers highlights the challenges of accounting for the interactions between climate, society, and technology. Source : https://venturebeat.com/ai/how-much-energy-does-ai-use-compared-to-humans-surprising-study-ignites-controversy/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

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I've recently started working on a project to create an entire episode of a cartoon show that stopped airing a while back. I've run into some trouble though in finding a program that can accurately recreate the character. Does anyone know what app or website I could use for this, or am I coming at this all wrong? submitted by /u/therabbitinthehat2 [link] [comments]  ( 9 min )

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Within the next two to three years no code AI co-pilots will enable exponentially more people to enter the AI app marketplace . The opportunity to create apps without needing to know how to code or hire a technical team to build them is a powerful game changer that will vastly expand the field. Many of the most pressing problems of our world - ripe for revolutionary new AI innovations - can in a very real sense be described as sociological and psychological in nature. For example, It has been said that climate change is much more of a moral issue than a technological one. Once we summon the will to address climate change, we will do what needs to be done. What this means is that sociologists, psychologists, anthropologists, economists and other social scientists will very soon be able to…  ( 10 min )

Couldn't find anything that works submitted by /u/Marvellover13 [link] [comments]  ( 8 min )

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In the previous post I mentioned that a particular Mersenne prime would be unsuitable for cryptography. In fact, all Mersenne primes are unsuitable for cryptography. A prime number p is called “safe” if p = 2q + 1 where q is also a prime. Safe primes are called safe because p − 1 does not […] Mersenne primes are unsafe first appeared on John D. Cook.  ( 6 min )

Electronic computers were invented before public key cryptography. Would public key cryptography have been possible before computers? The security of RSA encryption depends on the ratio of the difficulty of factoring relative to the difficulty of multiplication. This ratio was high, maybe higher, before modern computers. Suppose the idea of RSA encryption had occurred to […] Victorian public key cryptography first appeared on John D. Cook.  ( 6 min )

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​ slide as part of the presentation. I've listened to the entire lecture and am now going through these slides and I didn't understand the solution provided for the choices above in the image. So far I understand it like this. Noise basically means that the agent will not always do the action that you say. You say left and it will go left only 50% of the time. it will choose other actions randomly. so, Why is the answer to the (c) and (d) questions (2) and (3) respectively? I understand the learning rate but not how risking the cliff is affected by the noise. submitted by /u/vestedpolecat [link] [comments]  ( 9 min )

https://pepe-web3.network submitted by /u/Beginning_Success208 [link] [comments]  ( 8 min )

For a potential school project, im currently exploring whether or not some of the success the transformer architecture has had in seq-to-seq applications and high-dimensional pattern recognition could be extended to certain (online) RL problems (mainly those with high dimensional environment as well as long-term planning). This could be done by using an augmented transformer as a function approximator, probably in combination with a SOTA online-rl algorithm (such as PPO, but if you know something that generally performs better do tell). Due to some of the problems associated with highly complex models and sample efficiency, I also thought about training the model using imitation learning first (which should be no problem with policy gradient methods afaik, though some slight adjustments would have to be made). For context, I'm thinking on benchmarking the approach using AlphaStar unplugged. However, when looking at current literature, only very few papers directly use transformers this way. Transformers seem to be very sample efficient and to generalize very well, but they are still only really used in a purely offline context (sometimes without directly using RL-techniques, such as with the Decision Transformer). And, if they are used in an online context, then only in some really intricate combination with other models (such as in AlphaStar). Is there a reason why the approach I am currently considering is not popular in literature? Thank you very much. submitted by /u/Omycron83 [link] [comments]  ( 9 min )

ChatGPT, Bard, GPT-4, and the like are often pitched as ways to retrieve information. The problem is they'll "retrieve" whatever you ask for, whether or not it exists. Tumblr user @indigofoxpaws sent me a few screenshots where they'd asked ChatGPT for an explanation of  ( 3 min )

AI Weirdness: the strange side of machine learning  ( 2 min )

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AI systems like large language models (LLMs) are good at generating sentences but do not understand the meaning of the language. LLMs have shown emergent abilities and can be used as aids to brainstorming. GPT-4, an LLM, has been found to beat humans in creativity tests. In an experiment, GPT-4 generated more, cheaper, and better ideas for a product than human students. A professional working with GPT-4 can generate ideas at a rate of about 800 ideas per hour, making them 40 times more productive than a human working alone. This technology is seen as a potential tool for corporations, similar to management consulting firms like McKinsey & Company. Source : https://www.theguardian.com/commentisfree/2023/sep/23/chatbots-ai-gpt-4-university-students-creativity submitted by /u/NuseAI [link] [comments]  ( 9 min )

I've got a use case where I have dozens of PDFs which contain information applicable to my job. I'm wondering if there is a tool that can search through them all at the same time looking for answers to questions that I type. And once it finds something, pull up the location so I can read further. It should be restricted to the information I give it. I've heard a lot of the large language models like chatgpt and claude can do this, but they are restricted in the amount of files I can upload. submitted by /u/Aggressive_Ad_507 [link] [comments]  ( 9 min )

TLDR: New training approach enables smaller AI models to achieve state-of-the-art translation performance Large AI models like GPT-3 have good performance on translation tasks, but some smaller models struggle. Researchers from Johns Hopkins and Microsoft propose a new 2-stage fine-tuning method called ALMA that unlocks stronger translation abilities in smaller models with just 7-13 billion parameters. How it works: Fine-tune on monolingual data in non-English languages to improve comprehension Further fine-tune on small sets of high-quality human-translated parallel text The authors claim this achieves SOTA-level translation using far less data and compute than conventional methods: Matches performance of 175B parameter GPT-3 and 54B parameter NLLB with only 7-13B parameters Reaches NLLB-level quality with just 1 billion monolingual tokens and 18 hours of training I think this shows that smaller models can reach SOTA translation with specialized fine-tuning, so we may not need endlessly bigger datasets and models to get better performance. Looks like deliberate tuning targeting key language skills could be more important. Full summary here. Paper (preprint) is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

Yes, I know this topic has been beaten to death but entertain me (no pun intended) for just a few minutes. So yes, it's obvious that we have pretty advanced AI-powered applications that can generate images, music, short stories, hell even objects for video games. I'm curious as to how crazy this is gonna get in the coming decade or even shorter like the next 4 to 5 years. I mean look at AI-generated images now, they're getting more and more sophisticated across various different styles of art. I think it's only a matter of time where you could take a certain image of a character or something tell the app "Hey make the same image but make the character's arm raised slightly to the left here" and bam all of a sudden you have an animation (and this may already be possible). Add to that AI-ge…  ( 10 min )

Hi everyone 👋🤗 Iv made a short ai documentary Here is a small part of it Hope you enjoy it For the full clip you can check out https://youtu.be/uIdb5VELpio?si=uvqiw0hyTyPBHtjP submitted by /u/DigitalEffectsAI [link] [comments]  ( 8 min )

https://www.louisbouchard.ai/research-papers/ In this article are shared the best tips and practical tools I use daily to simplify my life as an engineer/researchers to be more efficient when looking for interesting research papers and reading them TLDR the tools discussed: - 42 Papers — Find trending papers - Connected Papers — Create a visual graph with your paper’s citations’ relations. - CatalyzeX — Get code for papers directly on Google, Arxiv, Scholar, Twitter, and more - Daily Papers — Find trending papers on Twitter - Papers With Code — Find papers for your task with code! - Crossmind — Video explanations for many Arxiv papers - Yannic Kilcher — Great youtube channel covering AI papers - What’s AI— Great youtube channel covering AI papers - Letitia — Great youtube channel covering AI papers - Two Minute Papers — Great youtube channel giving a quick overview of AI papers ​ Please, let me know if you use any other tools that I did not mention in my article that could be of great addition? submitted by /u/MLtinkerer [link] [comments]  ( 9 min )

I'm often using Chat GPT or Bing (Bard is not her available in my country) when I'm looking for something that is relatively complex in the scientific or technical field and I don't have enough background to make a keywords research or when the question is too complex for it. Then, for scientific topics, I try to get a confirmation with Google using the keywords in the generated answer and google. For technical topics I can often check if it works. Some examples: For cooking, a topic I don't master enough to ask questions with the best keywords... Are the spicy compounds of Ginger soluble in water or oil ? > I will get me to know that "the gingerol is liposoluble", what can be more easily checked in Google. Or for 3D geometry, a topic where I have complex questions... I want to place a 3D frame B so that the Z axis of the B frame point towards the world origin A. I know the angle Alpha between the Z axis of the frame B and The X,Y plane of A. I know the angle Beta between the X axis of A and the vector that goes from the center of A towards the projection of the center of B on the X,Y plane of A. How do you compute the homogeneous rotation matrix of B relative to A, knowing Alpha, Beta and the distance between the center of A and the center of B ? Most of time, Bing and the free ChatGPT can answer. But sometimes they struggle or give incorrect answer. Would ChatGPT plus paid subscription be helpful to find more accurate answers? submitted by /u/x11ry0 [link] [comments]  ( 9 min )

Sure, here's a comedic take on the concept of free will versus determinism, framed as a conversation between God and a human named Bob. Scene: Heaven's Control Room—Giant monitors display various life paths and decision trees. God: (looking over the monitors) Ah, Bob, welcome to Heaven's Control Room! This is where the magic happens. Or should I say, determinism? Bob: Wow, this place is amazing! But wait, why are we talking about determinism? I thought I had free will to make choices in my life. God: (chuckles) Ah, free will! Humanity's favorite illusion. Have a seat; we need to go over a few things. Bob: Illusion? I chose to wear mismatched socks today, didn't I? God: (grinning) You think that was a free choice? Ever heard of quantum mechanics? Your socks are mismatched at the sub…  ( 10 min )

Hi everyone, been playing with tortoise all day and trying to get a clone of my voice as close as possible and nothing seems to work. I'm thinking maybe I need to use my phone to record my voice and pass it into tortoise but I'm not sure of how to get the wav files from my phone at the recommended quality. Has anybody had really good luck getting tortoise to mimic you very closely? submitted by /u/MaxxMarketTrades [link] [comments]  ( 9 min )

I'm using LGBM for regression, where the Target column's lagged values (7 columns for each lag day) are also used as predictors when training the model. Absence of the 7Day lag values severely increases MAE value. Now when using the model in production, if I use the complete data as training dataset, how to get the 7day lag value of the time period I'm planning to predict? I obviously won't have the target value, to calculate it's 7Day lag value. What to do now? To explain in more detail: So, I'm predicting sales amount (Target variable y). The model is trained on 20 predictors (X), and 7 of them are the lag value of the Target Variable, i.e. Sales Amount. The thing is, while preparing the model, I had access to both X & y dataset, thus I could easily calculate Y's Lag values. Now, when predicting for future timestamps, I won't be having y. So how do I calculate the lag values, which is required in the trained model's predictor columns now? submitted by /u/boredmonki [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2309.07124 Abstract: Large language models (LLMs) often demonstrate inconsistencies with human preferences. Previous research gathered human preference data and then aligned the pre-trained models using reinforcement learning or instruction tuning, the so-called finetuning step. In contrast, aligning frozen LLMs without any extra data is more appealing. This work explores the potential of the latter setting. We discover that by integrating self-evaluation and rewind mechanisms, unaligned LLMs can directly produce responses consistent with human preferences via self-boosting. We introduce a novel inference method, Rewindable Auto-regressive INference (RAIN), that allows pre-trained LLMs to evaluate their own generation and use the evaluation results to guid…  ( 9 min )

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Hello everyone so I wanted to get into machinelearning and learn about ai Can someone help me with a roadmap, I would be really thankful submitted by /u/Fooda234 [link] [comments]  ( 8 min )

Hello, I'm currently preparing for oral exams in which I'll be evaluated on my understanding and proficiency in using Decision Trees, Random Forests, Neural Networks, and Support Vector Machines for various machine learning tasks (mostly spatial data). I'm contacting this community to gain valuable insights and guidance to excel in these exams. What are some crucial lessons you've learned in your machine-learning journey? Whether it's about model selection, data preprocessing, or debugging, I'm all ears for your experiences. What are some rules you have learned through practical work that are not so extensively described in classical literature? What are some mistakes that even professionals make when developing machine learning models? What are some common pitfalls to avoid when training neural networks? Thanks : submitted by /u/Aim_F0r_The_Moon [link] [comments]  ( 9 min )

Im trying to create a graph transformer-based model for de novo drug design (using graph transformer because I want to implement 3D data). I currently have 2 potential sources of primary data: PDBbind and CrossDocked2020. This would provide the protein-ligand structures. PDBbind is a more robust and higher quality dataset from what I know, and easier to work with. The problem is that it only contains about 20,000 complexes, and I'm not sure if that is enough for training a transformer. CrossDocked2020 contains millions of entries but I'm not sure about the quality and ease of use. Another dilemma is that I need/want to use a multi-task learning approach where the model is also being trained on bioactivity data, not just the structural information. This would require supplementation from sources like PubChem, ChEMBL, BDB, etc. and then I would need to align the data so it all matches up. If anyone can provide some guidance I'd really appreciate it. submitted by /u/Present_Network1959 [link] [comments]  ( 9 min )

I am supposed to be delivering a 5 minute presentation on tree-models as part of coursework requirement to a graduate class ON machine learning that I am enrolled in and I couldn't be more stressed! This is my first time 'learning' ML and I don't even know what I don't know about this topic!! If you were attending my presentation on tree models, what would you like to see (assuming this is a new topic for you too. but please provide feedback based on your [hopefully] extensive knowledge on this topic)? Here is what I have so far (based on google searches, some papers): -Introduction -Types of trees based on algo and techniques (basically classification trees and regression trees) -Then I am thinking of going off on a tangent about decision trees bec I have no clue about how to move this presentation forward -Real-world applications -Key takeaways (inserts clownface emoji) My prof asks the students questions about the topic as well. I am mostly concerned about WHAT to cover in 5 minutes without making look under-researched. Any redirection/suggestions will be appreciated! ​ submitted by /u/toomanymouthstofeed [link] [comments]  ( 9 min )

(I'm a Software Engineer who knows almost nothing about ML / NLP, so, apologies in advance if this doesn't make any sense.) I had a shower thought around if tokenization could incorporate meaning, so the models could be trained on "thoughts" instead of subword tokens (which is probably closer to how we humans think). To expand a little bit, we could first cluster all the words (maybe using the current day LLMs), then each token (or "thought") in the new scheme could represent a group of related words, followed by refinements that would be less and less important. For example, (cop_thought, cop_refinement) -> cop, but (cop_thought, police_refinement) -> police etc. So the encoding step would involve an LLM (which could possibly be relatively smaller), whose output would go to the actual model but the decoding would still be fairly straightforward. This could possibly free up some additional capacity in the actual model, assuming this heavy lifting tokenization makes its job easier, but as I'm typing it, I guess I'm simply moving some of the semantic understand happening in the hidden layers of the current day LLMs explicitly to the tokenization (encoding) step, which may not really change anything (if not make it worse). I'm still curious what folks think, if there's any related efforts (and all the ways I'm wrong -- https://meta.wikimedia.org/wiki/Cunningham%27s_Law). Thanks! submitted by /u/avamsi [link] [comments]  ( 9 min )

Keep in mind I'm very new to machine learning. I have an idea for a project where I train an AI to detect instances of someone being highly confident in a treatment for a condition, maybe biased toward more unconventional treatments. I was thinking that there may be many instances of people who posted that they were cured or nearly cured of such and such disease by an unconventional treatment on a forum about the condition. It may have been discussed for a little while, but ultimately buried in the pages of the forum. The plan would be to annotate instances I can find of such scenarios, maybe like this: [FIRST_PERSON_HIGH_CONFIDENCE_TREATMENT]I had severe [CONDITION]migraines[/CONDITION] for years, and nothing seemed to work. Then I tried [TREATMENT]grounding, where I walk barefoot on grass for 30 minutes every day[/TREATMENT]. I can [HIGH_CONFIDENCE]honestly say I've never felt better[/HIGH_CONFIDENCE]. My [CONDITION]migraines[/CONDITION] have reduced by 80%, and I'm [HIGH_CONFIDENCE]convinced this is a game-changer for me[/HIGH_CONFIDENCE].[/FIRST_PERSON_HIGH_CONFIDENCE_TREATMENT] Then train an AI with that data, so that it could detect cases of a person talking about themselves (to avoid hearsay) and saying, with high confidence, that a treatment worked for a condition. Then millions of forum posts could be fed to the AI to detect these, and the resulting data could be used to possibly discover effective treatments that are not in the mainstream. Any tips on getting started? I know almost nothing about this kind of stuff, like what models I should use, how to annotate it best (should I use relational labels?), whether to use a transformer or something else, stuff like that. Suggestions for books or other resources fit for a beginner that could help me learn how this could be done would be great too. submitted by /u/carbonflow45 [link] [comments]  ( 9 min )

I'm currently diving into the world of machine learning and transformers, and I'm trying to wrap my head around the concept of "attention" in transformer models. I've been reading papers and documentation, but I'm still struggling to fully grasp it. My Struggle: I get that attention involves multiplying "query" and "key" vectors to determine the importance of different words in a sequence, but I don't quite understand why this multiplication gives us a meaningful metric for importance. What I'm looking for: I'm comfortable with moderate level technicalities but require a deeper insight into the inner workings and rationale behind these mechanisms. Please share any insights, analogies, or technical details that can shed light on this concept. Thanks a bunch! submitted by /u/GraphicsMonster [link] [comments]  ( 9 min )

99.7% of its 8000 moves were legal with the longest game going 147 moves. You can test it here: https://github.com/adamkarvonen/chess_gpt_eval ​ https://preview.redd.it/821ydy7521qb1.png?width=1000&format=png&auto=webp&s=da6c96feaa527d0b7dfbf407bdc0210f3fcf947b More details here: https://twitter.com/a_karvonen/status/1705340535836221659 submitted by /u/seraine [link] [comments]  ( 9 min )

​ Hi everyone, I am working on a project that involves machine learning and computer vision. I want to train a model that can recognize and classify different types of signals used by the Indian railways. For this, I need a large and diverse image dataset of railway signals from various locations, angles, lighting conditions, etc. I have searched online for existing datasets, but I could not find any that suit my needs. So I wish to create my own dataset from scratch. However, I am not sure how to go about it. What are the best practices and tools for creating an image dataset? How do I collect, label, and organize the images? How do I ensure the quality and consistency of the data? submitted by /u/Responsible-Diver226 [link] [comments]  ( 9 min )

Hello, I am starting a ML course soon in college and I wanted to get a head start on the math part of things, since they keep saying the course is math heavy and hard. I know that it involves quite a lot of Linear Algebra, Calculus, and Probability and statistics, but what concepts in particular does ML focus on? If anyone has any Youtube or Udemy courses, as I have access to those, I would really appreciate it. For starters, I’m really aiming to just at least tackle the ML-specific math concepts. Thanks. submitted by /u/CrunchyMind [link] [comments]  ( 9 min )

TLDR: New training approach proposed to help smaller AI models to achieve state-of-the-art translation performance Large AI models like GPT-3 have good performance on translation tasks, but some smaller models struggle. Researchers from Johns Hopkins and Microsoft propose a new 2-stage fine-tuning method called ALMA that unlocks stronger translation abilities in smaller models with just 7-13 billion parameters. How it works: Fine-tune on monolingual data in non-English languages to improve comprehension Further fine-tune on small sets of high-quality human-translated parallel text The authors claim this achieves SOTA-level translation using far less data and compute than conventional methods: Matches performance of 175B parameter GPT-3 and 54B parameter NLLB with only 7-13B parameters Reaches NLLB-level quality with just 1 billion monolingual tokens and 18 hours of training I think this shows that smaller models can reach SOTA translation with specialized fine-tuning, so we may not need endlessly bigger datasets and models to get better performance. Looks like deliberate tuning targeting key language skills could be more important. Full summary here. Paper (preprint) is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

I've been reading the HyperDreamBooth paper and am confused about the number of parameters and corresponding matrix shapes in section 4.1 and figure 3 (below). ​ Figure 3 from the paper Maybe there's something more than just matrix multiplication, because the numbers don't add up. If there are two matrices, A & B, of size n,r and r,m, and r = 1, and you multiply them, then it implies that n + m = 386k, which seems to be a bit much for a number of parameters in a single layer. Then we have two matrices of size a,r and r,b, where a = 100, b = 50 and there are 28k variables, according to the figure - in 4.1 they say 30k. If there's 30k, that would imply r = 200, for matrices of shape 100,200 and 200,50. I guess 386k and 28k are for the whole models, n and m differ from layer to layer, and r stays at one. Quite surprising to me that approximating a n,m shaped matrix with a product of n,1 and 1,m vectors would work. Even more surprising that apparently you could squeeze it further to 100,1 and 50,1. submitted by /u/Foxtr0t [link] [comments]  ( 9 min )

Hey everyone! Multi-part question. I have scraped text (I repeat, text, and not structured data such as tables or something) from a medical site, and I want to know how to clean it. And when I say clean, I don't mean removal of html tags and such. I already have the paragraphs in plain text but there is a lot of spammy stuff like "You are not signed in; subscribe to this newsletter; by checking this box, I agree to the terms and conditions, etc." This text is not the exact same in all the paragraphs but there is high similarity. I would have thought there would be many tools to clean text and remove unrelated chunks like these but all I have been able to find has to do with cleaning html tags, changing date-time format and so on. Am I missing something or is this actually difficult? Secondly, the spammy text I mentioned is from just one site. I will be eventually scaling to many sites and god knows what random text I'll have to clean then. I used OpenAI embeddings and cosine similarity on the medical text to find similar paragraphs. The results were not great. Is there a way to improve the similarity search? I will be trying FAISS next but wanted to know what else I can do. It was suggested to me to use a pretrained embedding model specific to medical data. However, I found only one such model which is 20 gigs! I'm just getting started with these, so, appreciate any help I can get. Thanks a ton! submitted by /u/yipra97 [link] [comments]  ( 9 min )

Hey folks - I have been tasked with a project at work that is outside of my typical realm (non technical background), and I would love any and all insight on it! I have been asked to compare the top ten llms for research before we implement an llm for our company. So far my research has felt scattered because I’m not what directions to go in. I’ve been looking at things like open source vs closed source, parameters, tokens, what the license looks like (available for commercial use), and pricing. If anyone has thoughts on resources to look at or better ways to approach this, I would really appreciate it! submitted by /u/Greatvalueaidybryant [link] [comments]  ( 9 min )

Relatively novice ML practitioner here. My research is on various medical image segmentation problems, including brain 3D US (glioma), lung CT (interstitial lung disease in scleroderma patients), etc. using the PyTorch ecosystem (probably including frameworks such as MONAI) I'll have to conduct several experiments on various model architectures on parameters in the coming months. Specifically, these are what I'm gonna need: Experiment tracking (model architecture, training configuration, hyperparameters, evaluation metrics) Model storage (would be nice if there's a better way to store my model's parameters other than storing tons of .pth file on my harddisk or google drive) (Optional) Visualization (sample predictions of the model on the training or validation sets, maybe every 20 epochs or sth) Would like to hear any suggestions from the community I've found wandb, clearML, neptune, and Aim; but trying each of them individually would be too time-consuming considering my current schedule. Thanks in advance! submitted by /u/mimivirus2 [link] [comments]  ( 9 min )

submitted by /u/jarekduda [link] [comments]  ( 8 min )

I found an interesting arxiv paper mentioning that some optimizers can occur numerical instability for training neural network. Link: https://arxiv.org/abs/2307.16189 This can be a simple approach for low-precision neural network with 16-bit and future 8-bit or 4-bit. submitted by /u/Klutzy_Divide3485 [link] [comments]  ( 9 min )

I like generating long LaTeX documents from org-mode because, for one thing, org-mode has nice section folding. But not everyone I work with uses Emacs, so its better to work in LaTeX directly rather than have Emacs generate LaTeX. AUCTeX has section folding for LaTeX documents, though so far I’ve only has limited success at […] Navigating a LaTeX file first appeared on John D. Cook.  ( 5 min )

It’s surprisingly hard to find a complete list of HTML entities in the form of a data file. There are numerous sites that give lists, often incomplete, in a page formatted to be human-readable but not machine-readable. Here’s an XML file from the W3C. Here’s a two-column text file I created from the W3C data. HTML entity data first appeared on John D. Cook.  ( 5 min )

I’ve needed to use double-struck capital letters lately, also called blackboard bold. There are a few quirks in how they are represented in Unicode and in HTML entities, so I’m leaving some notes for myself here and for anyone else who might need to look this up. Unicode The double-struck capital letters are split into […] Double-struck capital letters first appeared on John D. Cook.  ( 6 min )

“Sometimes, you don’t know where you’re going until you get there.” – Schmarzo-ism? Yes, writing this blog turned into a journey. I started in one direction, but after several twists and turns, I ended up with this concept – that use case-centric language models can be combined into entity-centric language models that can support multiple… Read More »Use Case Language Models: Taming the LLM Beast – Part 1 The post Use Case Language Models: Taming the LLM Beast – Part 1 appeared first on Data Science Central.  ( 22 min )

Hi has anyone ever attempted to do RL with comsol multiphysics or any other FEM based simulation tool? submitted by /u/Practical_Ad_8782 [link] [comments]  ( 8 min )

Hi all, I'd like to share some of what I've learned over the last ~year getting up to speed with Python and RL. Mini RL Lab is a setup and workflow that works well for me to debug and experiment with concepts like agent algorithms, world models, planning, plasticity, transformers etc, and other beginners might find it a useful starting point for their own experiments. Link: https://github.com/modelbased/minirllab submitted by /u/thiagoazevedo [link] [comments]  ( 9 min )

As I understand, like in the example rock paper scissors, they compute the regret based on the last state. or in poker, they compute regret based on what last happened. But is it possible to compute regret for two or more moves ago? like "i wish when I saw 10 moves ago, I did x"? or has it been possible from the start and I just understood counterfactual regret minimization wrongly? submitted by /u/oniongarlic88 [link] [comments]  ( 9 min )

Hey everyone, I've been taking a look at hypernetworks and noticed they've been used in some cool ways in RL with stuff like Qmix and this Metalearning paper. Quick recap: f is our regular neural network: takes in input x and outputs y using weights \theta. g is the hypernetwork, it creates the weights \theta for f based on its own weights \phi and maybe the input x. Original paper: https://arxiv.org/abs/1609.09106 I watched this talk and it hinted that hypernetworks might even be better than our regular networks even for supervised learning regular tasks. So, I'm curious: What's the deal with hypernetworks? What makes them good or not vs regular neural networks? I get that they're good for metalearning, but could they also be a game-changer for other things, like sample efficiency? Does anyone have thoughts or reads on this? Thanks! ​ ​ submitted by /u/LazyButAmbitious [link] [comments]  ( 9 min )

DON'T KNOW IF THIS POST BELONGS HERE BUT... I have zero knowledge about the AI/ML. And the thing is my college is asking us to do projects on Deep learning. They specifically asked us to pick a base paper from ACM Journals or IEEE Transactions which has been published after 2020. And implement these papers and do some novelty work. And I have zero clue how to proceed. submitted by /u/um2_doma [link] [comments]  ( 9 min )

A fundamental shortcoming of the concept of Nash equilibrium is its computational intractability: approximating Nash equilibria in normal-form games is PPAD-hard. In this paper, inspired by the ideas of smoothed analysis, we introduce a relaxed variant of Nash equilibrium called $\sigma$-smooth Nash equilibrium, for a smoothness parameter $\sigma$. In a $\sigma$-smooth Nash equilibrium, players only need to achieve utility at least as high as their best deviation to a $\sigma$-smooth strategy, which is a distribution that does not put too much mass (as parametrized by $\sigma$) on any fixed action. We distinguish two variants of $\sigma$-smooth Nash equilibria: strong $\sigma$-smooth Nash equilibria, in which players are required to play $\sigma$-smooth strategies under equilibrium play, and weak $\sigma$-smooth Nash equilibria, where there is no such requirement. We show that both weak and strong $\sigma$-smooth Nash equilibria have superior computational properties to Nash equilibria: when $\sigma$ as well as an approximation parameter $\epsilon$ and the number of players are all constants, there is a constant-time randomized algorithm to find a weak $\epsilon$-approximate $\sigma$-smooth Nash equilibrium in normal-form games. In the same parameter regime, there is a polynomial-time deterministic algorithm to find a strong $\epsilon$-approximate $\sigma$-smooth Nash equilibrium in a normal-form game. These results stand in contrast to the optimal algorithm for computing $\epsilon$-approximate Nash equilibria, which cannot run in faster than quasipolynomial-time. We complement our upper bounds by showing that when either $\sigma$ or $\epsilon$ is an inverse polynomial, finding a weak $\epsilon$-approximate $\sigma$-smooth Nash equilibria becomes computationally intractable.  ( 3 min )

Pneumonia is the leading infectious cause of infant death in the world. When identified early, it is possible to alter the prognosis of the patient, one could use imaging exams to help in the diagnostic confirmation. Performing and interpreting the exams as soon as possible is vital for a good treatment, with the most common exam for this pathology being chest X-ray. The objective of this study was to develop a software that identify the presence or absence of pneumonia in chest radiographs. The software was developed as a computational model based on machine learning using transfer learning technique. For the training process, images were collected from a database available online with children's chest X-rays images taken at a hospital in China. After training, the model was then exposed to new images, achieving relevant results on identifying such pathology, reaching 98% sensitivity and 97.3% specificity for the sample used for testing. It can be concluded that it is possible to develop a software that identifies pneumonia in chest X-ray images.  ( 2 min )

3D visual grounding is a critical skill for household robots, enabling them to navigate, manipulate objects, and answer questions based on their environment. While existing approaches often rely on extensive labeled data or exhibit limitations in handling complex language queries, we propose LLM-Grounder, a novel zero-shot, open-vocabulary, Large Language Model (LLM)-based 3D visual grounding pipeline. LLM-Grounder utilizes an LLM to decompose complex natural language queries into semantic constituents and employs a visual grounding tool, such as OpenScene or LERF, to identify objects in a 3D scene. The LLM then evaluates the spatial and commonsense relations among the proposed objects to make a final grounding decision. Our method does not require any labeled training data and can generalize to novel 3D scenes and arbitrary text queries. We evaluate LLM-Grounder on the ScanRefer benchmark and demonstrate state-of-the-art zero-shot grounding accuracy. Our findings indicate that LLMs significantly improve the grounding capability, especially for complex language queries, making LLM-Grounder an effective approach for 3D vision-language tasks in robotics. Videos and interactive demos can be found on the project website https://chat-with-nerf.github.io/ .  ( 2 min )

Adversarial examples, deliberately crafted using small perturbations to fool deep neural networks, were first studied in image processing and more recently in NLP. While approaches to detecting adversarial examples in NLP have largely relied on search over input perturbations, image processing has seen a range of techniques that aim to characterise adversarial subspaces over the learned representations. In this paper, we adapt two such approaches to NLP, one based on nearest neighbors and influence functions and one on Mahalanobis distances. The former in particular produces a state-of-the-art detector when compared against several strong baselines; moreover, the novel use of influence functions provides insight into how the nature of adversarial example subspaces in NLP relate to those in image processing, and also how they differ depending on the kind of NLP task.  ( 2 min )

Bayesian Optimization (BO) is an effective approach for global optimization of black-box functions when function evaluations are expensive. Most prior works use Gaussian processes to model the black-box function, however, the use of kernels in Gaussian processes leads to two problems: first, the kernel-based methods scale poorly with the number of data points and second, kernel methods are usually not effective on complex structured high dimensional data due to curse of dimensionality. Therefore, we propose a novel black-box optimization algorithm where the black-box function is modeled using a neural network. Our algorithm does not need a Bayesian neural network to estimate predictive uncertainty and is therefore computationally favorable. We analyze the theoretical behavior of our algorithm in terms of regret bound using advances in NTK theory showing its efficient convergence. We perform experiments with both synthetic and real-world optimization tasks and show that our algorithm is more sample efficient compared to existing methods.  ( 2 min )

Developers expend a significant amount of time in editing code for a variety of reasons such as bug fixing or adding new features. Designing effective methods to predict code edits has been an active yet challenging area of research due to the diversity of code edits and the difficulty of capturing the developer intent. In this work, we address these challenges by endowing pre-trained large language models (LLMs) of code with the knowledge of prior, relevant edits. The generative capability of the LLMs helps address the diversity in code changes and conditioning code generation on prior edits helps capture the latent developer intent. We evaluate two well-known LLMs, Codex and CodeT5, in zero-shot and fine-tuning settings respectively. In our experiments with two datasets, the knowledge of prior edits boosts the performance of the LLMs significantly and enables them to generate 29% and 54% more correctly edited code in top-1 suggestions relative to the current state-of-the-art symbolic and neural approaches, respectively.  ( 2 min )

Denoising diffusion probabilistic models (DDPMs) have shown promising performance for speech synthesis. However, a large number of iterative steps are required to achieve high sample quality, which restricts the inference speed. Maintaining sample quality while increasing sampling speed has become a challenging task. In this paper, we propose a "Co"nsistency "Mo"del-based "Speech" synthesis method, CoMoSpeech, which achieve speech synthesis through a single diffusion sampling step while achieving high audio quality. The consistency constraint is applied to distill a consistency model from a well-designed diffusion-based teacher model, which ultimately yields superior performances in the distilled CoMoSpeech. Our experiments show that by generating audio recordings by a single sampling step, the CoMoSpeech achieves an inference speed more than 150 times faster than real-time on a single NVIDIA A100 GPU, which is comparable to FastSpeech2, making diffusion-sampling based speech synthesis truly practical. Meanwhile, objective and subjective evaluations on text-to-speech and singing voice synthesis show that the proposed teacher models yield the best audio quality, and the one-step sampling based CoMoSpeech achieves the best inference speed with better or comparable audio quality to other conventional multi-step diffusion model baselines. Audio samples are available at https://comospeech.github.io/.  ( 3 min )

Machine learning models trained with structural health monitoring data have become a powerful tool for system identification. This paper presents a physics-informed Gaussian process (GP) model for Timoshenko beam elements. The model is constructed as a multi-output GP with covariance and cross-covariance kernels analytically derived based on the differential equations for deflections, rotations, strains, bending moments, shear forces and applied loads. Stiffness identification is performed in a Bayesian format by maximising a posterior model through a Markov chain Monte Carlo method, yielding a stochastic model for the structural parameters. The optimised GP model is further employed for probabilistic predictions of unobserved responses. Additionally, an entropy-based method for physics-informed sensor placement optimisation is presented, exploiting heterogeneous sensor position information and structural boundary conditions built into the GP model. Results demonstrate that the proposed approach is effective at identifying structural parameters and is capable of fusing data from heterogeneous and multi-fidelity sensors. Probabilistic predictions of structural responses and internal forces are in closer agreement with measured data. We validate our model with an experimental setup and discuss the quality and uncertainty of the obtained results. The proposed approach has potential applications in the field of structural health monitoring (SHM) for both mechanical and structural systems.  ( 3 min )

The proliferation of fake news has become a significant concern in recent times due to its potential to spread misinformation and manipulate public opinion. This paper presents a comprehensive study on detecting fake news in Brazilian Portuguese, focusing on journalistic-type news. We propose a machine learning-based approach that leverages natural language processing techniques, including TF-IDF and Word2Vec, to extract features from textual data. We evaluate the performance of various classification algorithms, such as logistic regression, support vector machine, random forest, AdaBoost, and LightGBM, on a dataset containing both true and fake news articles. The proposed approach achieves high accuracy and F1-Score, demonstrating its effectiveness in identifying fake news. Additionally, we developed a user-friendly web platform, fakenewsbr.com, to facilitate the verification of news articles' veracity. Our platform provides real-time analysis, allowing users to assess the likelihood of fake news articles. Through empirical analysis and comparative studies, we demonstrate the potential of our approach to contribute to the fight against the spread of fake news and promote more informed media consumption.  ( 3 min )

Rapid and accurate identification of Venous thromboembolism (VTE), a severe cardiovascular condition including deep vein thrombosis (DVT) and pulmonary embolism (PE), is important for effective treatment. Leveraging Natural Language Processing (NLP) on radiology reports, automated methods have shown promising advancements in identifying VTE events from retrospective data cohorts or aiding clinical experts in identifying VTE events from radiology reports. However, effectively training Deep Learning (DL) and the NLP models is challenging due to limited labeled medical text data, the complexity and heterogeneity of radiology reports, and data imbalance. This study proposes novel method combinations of DL methods, along with data augmentation, adaptive pre-trained NLP model selection, and a clinical expert NLP rule-based classifier, to improve the accuracy of VTE identification in unstructured (free-text) radiology reports. Our experimental results demonstrate the model's efficacy, achieving an impressive 97\% accuracy and 97\% F1 score in predicting DVT, and an outstanding 98.3\% accuracy and 98.4\% F1 score in predicting PE. These findings emphasize the model's robustness and its potential to significantly contribute to VTE research.  ( 2 min )

Conditional GANs are frequently used for manipulating the attributes of face images, such as expression, hairstyle, pose, or age. Even though the state-of-the-art models successfully modify the requested attributes, they simultaneously modify other important characteristics of the image, such as a person's identity. In this paper, we focus on solving this problem by introducing PluGeN4Faces, a plugin to StyleGAN, which explicitly disentangles face attributes from a person's identity. Our key idea is to perform training on images retrieved from movie frames, where a given person appears in various poses and with different attributes. By applying a type of contrastive loss, we encourage the model to group images of the same person in similar regions of latent space. Our experiments demonstrate that the modifications of face attributes performed by PluGeN4Faces are significantly less invasive on the remaining characteristics of the image than in the existing state-of-the-art models.  ( 2 min )

We consider probabilistic models for sequential observations which exhibit gradual transitions among a finite number of states. We are particularly motivated by applications such as human activity analysis where observed accelerometer time series contains segments representing distinct activities, which we call pure states, as well as periods characterized by continuous transition among these pure states. To capture this transitory behavior, the dynamical Wasserstein barycenter (DWB) model of Cheng et al. in 2021 [1] associates with each pure state a data-generating distribution and models the continuous transitions among these states as a Wasserstein barycenter of these distributions with dynamically evolving weights. Focusing on the univariate case where Wasserstein distances and barycenters can be computed in closed form, we extend [1] specifically relaxing the parameterization of the pure states as Gaussian distributions. We highlight issues related to the uniqueness in identifying the model parameters as well as uncertainties induced when estimating a dynamically evolving distribution from a limited number of samples. To ameliorate non-uniqueness, we introduce regularization that imposes temporal smoothness on the dynamics of the barycentric weights. A quantile-based approximation of the pure state distributions yields a finite dimensional estimation problem which we numerically solve using cyclic descent alternating between updates to the pure-state quantile functions and the barycentric weights. We demonstrate the utility of the proposed algorithm in segmenting both simulated and real world human activity time series.  ( 3 min )

This work introduces Physics-informed State-space neural network Models (PSMs), a novel solution to achieving real-time optimization, flexibility, and fault tolerance in autonomous systems, particularly in transport-dominated systems such as chemical, biomedical, and power plants. Traditional data-driven methods fall short due to a lack of physical constraints like mass conservation; PSMs address this issue by training deep neural networks with sensor data and physics-informing using components' Partial Differential Equations (PDEs), resulting in a physics-constrained, end-to-end differentiable forward dynamics model. Through two in silico experiments - a heated channel and a cooling system loop - we demonstrate that PSMs offer a more accurate approach than purely data-driven models. Beyond accuracy, there are several compelling use cases for PSMs. In this work, we showcase two: the creation of a nonlinear supervisory controller through a sequentially updated state-space representation and the proposal of a diagnostic algorithm using residuals from each of the PDEs. The former demonstrates the ability of PSMs to handle both constant and time-dependent constraints, while the latter illustrates their value in system diagnostics and fault detection. We further posit that PSMs could serve as a foundation for Digital Twins, constantly updated digital representations of physical systems.  ( 2 min )

This study introduces a novel hierarchical divisive clustering approach with stochastic splitting functions (SSFs) to enhance classification performance in multi-class datasets through hierarchical classification (HC). The method has the unique capability of generating hierarchy without requiring explicit information, making it suitable for datasets lacking prior knowledge of hierarchy. By systematically dividing classes into two subsets based on their discriminability according to the classifier, the proposed approach constructs a binary tree representation of hierarchical classes. The approach is evaluated on 46 multi-class time series datasets using popular classifiers (svm and rocket) and SSFs (potr, srtr, and lsoo). The results reveal that the approach significantly improves classification performance in approximately half and a third of the datasets when using rocket and svm as the classifier, respectively. The study also explores the relationship between dataset features and HC performance. While the number of classes and flat classification (FC) score show consistent significance, variations are observed with different splitting functions. Overall, the proposed approach presents a promising strategy for enhancing classification by generating hierarchical structure in multi-class time series datasets. Future research directions involve exploring different splitting functions, classifiers, and hierarchy structures, as well as applying the approach to diverse domains beyond time series data. The source code is made openly available to facilitate reproducibility and further exploration of the method.  ( 2 min )

To make effective decisions in novel environments with long-horizon goals, it is crucial to engage in hierarchical reasoning across spatial and temporal scales. This entails planning abstract subgoal sequences, visually reasoning about the underlying plans, and executing actions in accordance with the devised plan through visual-motor control. We propose Compositional Foundation Models for Hierarchical Planning (HiP), a foundation model which leverages multiple expert foundation model trained on language, vision and action data individually jointly together to solve long-horizon tasks. We use a large language model to construct symbolic plans that are grounded in the environment through a large video diffusion model. Generated video plans are then grounded to visual-motor control, through an inverse dynamics model that infers actions from generated videos. To enable effective reasoning within this hierarchy, we enforce consistency between the models via iterative refinement. We illustrate the efficacy and adaptability of our approach in three different long-horizon table-top manipulation tasks.  ( 2 min )

Session-based recommendation, aiming at making the prediction of the user's next item click based on the information in a single session only even in the presence of some random user's behavior, is a complex problem. This complex problem requires a high-capability model of predicting the user's next action. Most (if not all) existing models follow the encoder-predictor paradigm where all studies focus on how to optimize the encoder module extensively in the paradigm but they ignore how to optimize the predictor module. In this paper, we discover the existing critical issue of the low-capability predictor module among existing models. Motivated by this, we propose a novel framework called \emph{\underline{S}ession-based \underline{R}ecommendation with \underline{Pred}ictor \underline{A}dd-\underline{O}n} (SR-PredictAO). In this framework, we propose a high-capability predictor module which could alleviate the effect of random user's behavior for prediction. It is worth mentioning that this framework could be applied to any existing models, which could give opportunities for further optimizing the framework. Extensive experiments on two real benchmark datasets for three state-of-the-art models show that \emph{SR-PredictAO} out-performs the current state-of-the-art model by up to 2.9\% in HR@20 and 2.3\% in MRR@20. More importantly, the improvement is consistent across almost all the existing models on all datasets, which could be regarded as a significant contribution in the field.  ( 2 min )

Action scene understanding in soccer is a challenging task due to the complex and dynamic nature of the game, as well as the interactions between players. This article provides a comprehensive overview of this task divided into action recognition, spotting, and spatio-temporal action localization, with a particular emphasis on the modalities used and multimodal methods. We explore the publicly available data sources and metrics used to evaluate models' performance. The article reviews recent state-of-the-art methods that leverage deep learning techniques and traditional methods. We focus on multimodal methods, which integrate information from multiple sources, such as video and audio data, and also those that represent one source in various ways. The advantages and limitations of methods are discussed, along with their potential for improving the accuracy and robustness of models. Finally, the article highlights some of the open research questions and future directions in the field of soccer action recognition, including the potential for multimodal methods to advance this field. Overall, this survey provides a valuable resource for researchers interested in the field of action scene understanding in soccer.  ( 2 min )

The challenge in sourcing attribution for forgery faces has gained widespread attention due to the rapid development of generative techniques. While many recent works have taken essential steps on GAN-generated faces, more threatening attacks related to identity swapping or expression transferring are still overlooked. And the forgery traces hidden in unknown attacks from the open-world unlabeled faces still remain under-explored. To push the related frontier research, we introduce a new benchmark called Open-World DeepFake Attribution (OW-DFA), which aims to evaluate attribution performance against various types of fake faces under open-world scenarios. Meanwhile, we propose a novel framework named Contrastive Pseudo Learning (CPL) for the OW-DFA task through 1) introducing a Global-Local Voting module to guide the feature alignment of forged faces with different manipulated regions, 2) designing a Confidence-based Soft Pseudo-label strategy to mitigate the pseudo-noise caused by similar methods in unlabeled set. In addition, we extend the CPL framework with a multi-stage paradigm that leverages pre-train technique and iterative learning to further enhance traceability performance. Extensive experiments verify the superiority of our proposed method on the OW-DFA and also demonstrate the interpretability of deepfake attribution task and its impact on improving the security of deepfake detection area.  ( 2 min )

Sequential models, such as Recurrent Neural Networks and Neural Ordinary Differential Equations, have long suffered from slow training due to their inherent sequential nature. For many years this bottleneck has persisted, as many thought sequential models could not be parallelized. We challenge this long-held belief with our parallel algorithm that accelerates GPU evaluation of sequential models by up to 3 orders of magnitude faster without compromising output accuracy. The algorithm does not need any special structure in the sequential models' architecture, making it applicable to a wide range of architectures. Using our method, training sequential models can be more than 10 times faster than the common sequential method without any meaningful difference in the training results. Leveraging this accelerated training, we discovered the efficacy of the Gated Recurrent Unit in a long time series classification problem with 17k time samples. By overcoming the training bottleneck, our work serves as the first step to unlock the potential of non-linear sequential models for long sequence problems.  ( 2 min )

Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (MIL) is a common solution for working with high resolution images by classifying bags of objects (i.e. sets of smaller image patches). However, such processing is typically performed at a single scale (e.g., 20x magnification) of WSIs, disregarding the vital inter-scale information that is key to diagnoses by human pathologists. In this study, we propose a novel cross-scale MIL algorithm to explicitly aggregate inter-scale relationships into a single MIL network for pathological image diagnosis. The contribution of this paper is three-fold: (1) A novel cross-scale MIL (CS-MIL) algorithm that integrates the multi-scale information and the inter-scale relationships is proposed; (2) A toy dataset with scale-specific morphological features is created and released to examine and visualize differential cross-scale attention; (3) Superior performance on both in-house and public datasets is demonstrated by our simple cross-scale MIL strategy. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL.  ( 2 min )

Stochastic Gradient Descent (SGD), a widely used optimization algorithm in deep learning, is often limited to converging to local optima due to the non-convex nature of the problem. Leveraging these local optima to improve model performance remains a challenging task. Given the inherent complexity of neural networks, the simple arithmetic averaging of the obtained local optima models in undesirable results. This paper proposes a {\em soft merging} method that facilitates rapid merging of multiple models, simplifies the merging of specific parts of neural networks, and enhances robustness against malicious models with extreme values. This is achieved by learning gate parameters through a surrogate of the $l_0$ norm using hard concrete distribution without modifying the model weights of the given local optima models. This merging process not only enhances the model performance by converging to a better local optimum, but also minimizes computational costs, offering an efficient and explicit learning process integrated with stochastic gradient descent. Thorough experiments underscore the effectiveness and superior performance of the merged neural networks.

Biomedical image datasets can be imbalanced due to the rarity of targeted diseases. Generative Adversarial Networks play a key role in addressing this imbalance by enabling the generation of synthetic images to augment datasets. It is important to generate synthetic images that incorporate a diverse range of features to accurately represent the distribution of features present in the training imagery. Furthermore, the absence of diverse features in synthetic images can degrade the performance of machine learning classifiers. The mode collapse problem impacts Generative Adversarial Networks' capacity to generate diversified images. Mode collapse comes in two varieties: intra-class and inter-class. In this paper, both varieties of the mode collapse problem are investigated, and their subsequent impact on the diversity of synthetic X-ray images is evaluated. This work contributes an empirical demonstration of the benefits of integrating the adaptive input-image normalization with the Deep Convolutional GAN and Auxiliary Classifier GAN to alleviate the mode collapse problems. Synthetically generated images are utilized for data augmentation and training a Vision Transformer model. The classification performance of the model is evaluated using accuracy, recall, and precision scores. Results demonstrate that the DCGAN and the ACGAN with adaptive input-image normalization outperform the DCGAN and ACGAN with un-normalized X-ray images as evidenced by the superior diversity scores and classification scores.

This paper presents our solution for the Real Robot Challenge (RRC) III, a competition featured in the NeurIPS 2022 Competition Track, aimed at addressing dexterous robotic manipulation tasks through learning from pre-collected offline data. Participants were provided with two types of datasets for each task: expert and mixed datasets with varying skill levels. While the simplest offline policy learning algorithm, Behavioral Cloning (BC), performed remarkably well when trained on expert datasets, it outperformed even the most advanced offline reinforcement learning (RL) algorithms. However, BC's performance deteriorated when applied to mixed datasets, and the performance of offline RL algorithms was also unsatisfactory. Upon examining the mixed datasets, we observed that they contained a significant amount of expert data, although this data was unlabeled. To address this issue, we proposed a semi-supervised learning-based classifier to identify the underlying expert behavior within mixed datasets, effectively isolating the expert data. To further enhance BC's performance, we leveraged the geometric symmetry of the RRC arena to augment the training dataset through mathematical transformations. In the end, our submission surpassed that of all other participants, even those who employed complex offline RL algorithms and intricate data processing and feature engineering techniques.

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings.

Numerically solving partial differential equations typically requires fine discretization to resolve necessary spatiotemporal scales, which can be computationally expensive. Recent advances in deep learning have provided a new approach to solving partial differential equations that involves the use of neural operators. Neural operators are neural network architectures that learn mappings between function spaces and have the capability to solve partial differential equations based on data. This study utilizes a novel neural operator called Hyena, which employs a long convolutional filter that is parameterized by a multilayer perceptron. The Hyena operator is an operation that enjoys sub-quadratic complexity and state space model to parameterize long convolution that enjoys a global receptive field. This mechanism enhances the model's comprehension of the input's context and enables data-dependent weight for different partial differential equations instances. To measure how effective the layers are in solving partial differential equations, we conduct experiments on Diffusion-Reaction equation and Navier Stokes equation. Our findings indicate Hyena Neural operator can serve as an efficient and accurate model for learning partial differential equations solution operator. The data and code used can be found at: https://github.com/Saupatil07/Hyena-Neural-Operator  ( 2 min )

Federated Learning (FL) is a distributed machine learning technique that allows model training among multiple devices or organizations by sharing training parameters instead of raw data. However, adversaries can still infer individual information through inference attacks (e.g. differential attacks) on these training parameters. As a result, Differential Privacy (DP) has been widely used in FL to prevent such attacks. We consider differentially private federated learning in a resource-constrained scenario, where both privacy budget and communication round are constrained. By theoretically analyzing the convergence, we can find the optimal number of differentially private local iterations for clients between any two sequential global updates. Based on this, we design an algorithm of differentially private federated learning with adaptive local iterations (ALI-DPFL). We experiment our algorithm on the FashionMNIST and CIFAR10 datasets, and demonstrate significantly better performances than previous work in the resource-constraint scenario.

Serial femtosecond crystallography at X-ray free electron laser facilities opens a new era for the determination of crystal structure. However, the data processing of those experiments is facing unprecedented challenge, because the total number of diffraction patterns needed to determinate a high-resolution structure is huge. Machine learning methods are very likely to play important roles in dealing with such a large volume of data. Convolutional neural networks have made a great success in the field of pattern classification, however, training of the networks need very large datasets with labels. Th is heavy dependence on labeled datasets will seriously restrict the application of networks, because it is very costly to annotate a large number of diffraction patterns. In this article we present our job on the classification of diffraction pattern by weakly supervised algorithms, with the aim of reducing as much as possible the size of the labeled dataset required for training. Our result shows that weakly supervised methods can significantly reduce the need for the number of labeled patterns while achieving comparable accuracy to fully supervised methods.

With the advent of the IoT, AI and ML/DL algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. Consequently, the realm of data-driven medical applications has garnered significant attention spanning academia and industry, ushering in marked enhancements in healthcare delivery quality. Despite these strides, the adoption of AI-driven medical applications remains hindered by formidable challenges, including the arduous task of meeting security, privacy, and quality of service (QoS) standards. Recent developments in federated learning have made it possible to train complex machine-learned models in a distributed manner and has become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, this survey paper highlights the current and future of FL technology in medical applications where data sharing is a significant burden. We delve into the contemporary research trends and their outcomes, unravelling the intricacies of designing reliable and scalable FL models. Our survey outlines the foundational statistical predicaments of FL, confronts device-related obstacles, delves into security challenges, and navigates the intricate terrain of privacy concerns, all while spotlighting its transformative potential within the medical domain. A primary focus of our study rests on medical applications, where we underscore the weighty burden of global cancer and illuminate the potency of FL in engendering computer-aided diagnosis tools that address this challenge with heightened efficacy.

Real-world production systems often grapple with maintaining data quality in large-scale, dynamic streams. We introduce Drifter, an efficient and lightweight system for online feature monitoring and verification in recommendation use cases. Drifter addresses limitations of existing methods by delivering agile, responsive, and adaptable data quality monitoring, enabling real-time root cause analysis, drift detection and insights into problematic production events. Integrating state-of-the-art online feature ranking for sparse data and anomaly detection ideas, Drifter is highly scalable and resource-efficient, requiring only two threads and less than a gigabyte of RAM per production deployments that handle millions of instances per minute. Evaluation on real-world data sets demonstrates Drifter's effectiveness in alerting and mitigating data quality issues, substantially improving reliability and performance of real-time live recommender systems.

Learning control policies offline from pre-recorded datasets is a promising avenue for solving challenging real-world problems. However, available datasets are typically of mixed quality, with a limited number of the trajectories that we would consider as positive examples; i.e., high-quality demonstrations. Therefore, we propose a novel iterative learning algorithm for identifying expert trajectories in unlabeled mixed-quality robotics datasets given a minimal set of positive examples, surpassing existing algorithms in terms of accuracy. We show that applying behavioral cloning to the resulting filtered dataset outperforms several competitive offline reinforcement learning and imitation learning baselines. We perform experiments on a range of simulated locomotion tasks and on two challenging manipulation tasks on a real robotic system; in these experiments, our method showcases state-of-the-art performance. Our website: \url{https://sites.google.com/view/offline-policy-learning-pubc}.

Connectionist temporal classification (CTC) is commonly adopted for sequence modeling tasks like speech recognition, where it is necessary to preserve order between the input and target sequences. However, CTC is only applied to deterministic sequence models, where the latent space is discontinuous and sparse, which in turn makes them less capable of handling data variability when compared to variational models. In this paper, we integrate CTC with a variational model and derive loss functions that can be used to train more generalizable sequence models that preserve order. Specifically, we derive two versions of the novel variational CTC based on two reasonable assumptions, the first being that the variational latent variables at each time step are conditionally independent; and the second being that these latent variables are Markovian. We show that both loss functions allow direct optimization of the variational lower bound for the model log-likelihood, and present computationally tractable forms for implementing them.

We present a neural network for mitigating pseudorange bias to improve localization performance with data collected from Android smartphones. We represent pseudorange bias using a pragmatic satellite-wise Multiple Layer Perceptron (MLP), the inputs of which are six satellite-receiver-context-related features derived from Android raw Global Navigation Satellite System (GNSS) measurements. To supervise the training process, we carefully calculate the target values of pseudorange bias using location ground truth and smoothing techniques and optimize a loss function containing the estimation residuals of smartphone clock bias. During the inference process, we employ model-based localization engines to compute locations with pseudoranges corrected by the neural network. Consequently, this hybrid pipeline can attend to both pseudorange bias and noise. We evaluate the framework on an open dataset and consider four application scenarios for investigating fingerprinting and cross-trace localization in rural and urban areas. Extensive experiments demonstrate that the proposed framework outperforms model-based and state-of-the-art data-driven approaches.

This work presents a deep learning surrogate model for the fast simulation of high-dimensional frequency selective surfaces. We consider unit-cells which are built as multiple concatenated stacks of screens and their design requires the control over many geometrical degrees of freedom. Thanks to the introduction of physical insight into the model, it can produce accurate predictions of the S-parameters of a certain structure after training with a reduced dataset.The proposed model is highly versatile and it can be used with any kind of frequency selective surface, based on either perforations or patches of any arbitrary geometry. Numeric examples are presented here for the case of frequency selective surfaces composed of screens with rectangular perforations, showing an excellent agreement between the predicted performance and such obtained with a full-wave simulator.

The ability to classify images is dependent on having access to large labeled datasets and testing on data from the same domain that the model can train on. Classification becomes more challenging when dealing with new data from a different domain, where gathering and especially labeling a larger image dataset for retraining a classification model requires a labor-intensive human effort. Cross-domain classification frameworks were developed to handle this data domain shift problem by utilizing unsupervised image-to-image translation models to translate an input image from the unlabeled domain to the labeled domain. The problem with these unsupervised models lies in their unsupervised nature. For lack of annotations, it is not possible to use the traditional supervised metrics to evaluate these translation models to pick the best-saved checkpoint model. This paper introduces a new method called Domain-knowledge Inspired Pseudo Supervision (DIPS) which utilizes domain-informed Gaussian Mixture Models to generate pseudo annotations to enable the use of traditional supervised metrics. This method was designed specifically to support cross-domain classification applications contrary to other typically used metrics such as the FID which were designed to evaluate the model in terms of the quality of the generated image from a human-eye perspective. DIPS proves its effectiveness by outperforming various GAN evaluation metrics, including FID, when selecting the optimal saved checkpoint model. It is also evaluated against truly supervised metrics. Furthermore, DIPS showcases its robustness and interpretability by demonstrating a strong correlation with truly supervised metrics, highlighting its superiority over existing state-of-the-art alternatives. The code and data to replicate the results can be found on the official Github repository: https://github.com/Hindawi91/DIPS

Emerging real-time multi-model ML (RTMM) workloads such as AR/VR and drone control involve dynamic behaviors in various granularity; task, model, and layers within a model. Such dynamic behaviors introduce new challenges to the system software in an ML system since the overall system load is not completely predictable, unlike traditional ML workloads. In addition, RTMM workloads require real-time processing, involve highly heterogeneous models, and target resource-constrained devices. Under such circumstances, developing an effective scheduler gains more importance to better utilize underlying hardware considering the unique characteristics of RTMM workloads. Therefore, we propose a new scheduler, DREAM, which effectively handles various dynamicity in RTMM workloads targeting multi-accelerator systems. DREAM quantifies the unique requirements for RTMM workloads and utilizes the quantified scores to drive scheduling decisions, considering the current system load and other inference jobs on different models and input frames. DREAM utilizes tunable parameters that provide fast and effective adaptivity to dynamic workload changes. In our evaluation of five scenarios of RTMM workload, DREAM reduces the overall UXCost, which is an equivalent metric of the energy-delay product (EDP) for RTMM defined in the paper, by 32.2% and 50.0% in the geometric mean (up to 80.8% and 97.6%) compared to state-of-the-art baselines, which shows the efficacy of our scheduling methodology.

We introduce an extension to the CLRS algorithmic learning benchmark, prioritizing scalability and the utilization of sparse representations. Many algorithms in CLRS require global memory or information exchange, mirrored in its execution model, which constructs fully connected (not sparse) graphs based on the underlying problem. Despite CLRS's aim of assessing how effectively learned algorithms can generalize to larger instances, the existing execution model becomes a significant constraint due to its demanding memory requirements and runtime (hard to scale). However, many important algorithms do not demand a fully connected graph; these algorithms, primarily distributed in nature, align closely with the message-passing paradigm employed by Graph Neural Networks. Hence, we propose SALSA-CLRS, an extension of the current CLRS benchmark specifically with scalability and sparseness in mind. Our approach includes adapted algorithms from the original CLRS benchmark and introduces new problems from distributed and randomized algorithms. Moreover, we perform a thorough empirical evaluation of our benchmark. Code is publicly available at https://github.com/jkminder/SALSA-CLRS.

This paper proposes a supervised classification algorithm capable of continual learning by utilizing an Adaptive Resonance Theory (ART)-based growing self-organizing clustering algorithm. The ART-based clustering algorithm is theoretically capable of continual learning, and the proposed algorithm independently applies it to each class of training data for generating classifiers. Whenever an additional training data set from a new class is given, a new ART-based clustering will be defined in a different learning space. Thanks to the above-mentioned features, the proposed algorithm realizes continual learning capability. Simulation experiments showed that the proposed algorithm has superior classification performance compared with state-of-the-art clustering-based classification algorithms capable of continual learning.

The ultimate goal of artificial intelligence is to mimic the human brain to perform decision-making and control directly from high-dimensional sensory input. Diffractive optical networks provide a promising solution for implementing artificial intelligence with high-speed and low-power consumption. Most of the reported diffractive optical networks focus on single or multiple tasks that do not involve environmental interaction, such as object recognition and image classification. In contrast, the networks capable of performing decision-making and control have not yet been developed to our knowledge. Here, we propose using deep reinforcement learning to implement diffractive optical networks that imitate human-level decision-making and control capability. Such networks taking advantage of a residual architecture, allow for finding optimal control policies through interaction with the environment and can be readily implemented with existing optical devices. The superior performance of these networks is verified by engaging three types of classic games, Tic-Tac-Toe, Super Mario Bros., and Car Racing. Finally, we present an experimental demonstration of playing Tic-Tac-Toe by leveraging diffractive optical networks based on a spatial light modulator. Our work represents a solid step forward in advancing diffractive optical networks, which promises a fundamental shift from the target-driven control of a pre-designed state for simple recognition or classification tasks to the high-level sensory capability of artificial intelligence. It may find exciting applications in autonomous driving, intelligent robots, and intelligent manufacturing.

In many applications in data clustering, it is desirable to find not just a single partition into clusters but a sequence of partitions describing the data at different scales, or levels of coarseness. A natural problem then is to analyse and compare the (not necessarily hierarchical) sequences of partitions that underpin such multiscale descriptions of data. Here, we introduce a filtration of abstract simplicial complexes, denoted the Multiscale Clustering Filtration (MCF), which encodes arbitrary patterns of cluster assignments across scales, and we prove that the MCF produces stable persistence diagrams. We then show that the zero-dimensional persistent homology of the MCF measures the degree of hierarchy in the sequence of partitions, and that the higher-dimensional persistent homology tracks the emergence and resolution of conflicts between cluster assignments across the sequence of partitions. To broaden the theoretical foundations of the MCF, we also provide an equivalent construction via a nerve complex filtration, and we show that in the hierarchical case, the MCF reduces to a Vietoris-Rips filtration of an ultrametric space. We briefly illustrate how the MCF can serve to characterise multiscale clustering structures in numerical experiments on synthetic data.

Cities around the world face a critical shortage of affordable and decent housing. Despite its critical importance for policy, our ability to effectively monitor and track progress in urban housing is limited. Deep learning-based computer vision methods applied to street-level images have been successful in the measurement of socioeconomic and environmental inequalities but did not fully utilize temporal images to track urban change as time-varying labels are often unavailable. We used self-supervised methods to measure change in London using 15 million street images taken between 2008 and 2021. Our novel adaptation of Barlow Twins, Street2Vec, embeds urban structure while being invariant to seasonal and daily changes without manual annotations. It outperformed generic embeddings, successfully identified point-level change in London's housing supply from street-level images, and distinguished between major and minor change. This capability can provide timely information for urban planning and policy decisions toward more liveable, equitable, and sustainable cities.

We tackle the problem of robust novelty detection, where we aim to detect novelties in terms of semantic content while being invariant to changes in other, irrelevant factors. Specifically, we operate in a setup with multiple environments, where we determine the set of features that are associated more with the environments, rather than to the content relevant for the task. Thus, we propose a method that starts with a pretrained embedding and a multi-env setup and manages to rank the features based on their environment-focus. First, we compute a per-feature score based on the feature distribution variance between envs. Next, we show that by dropping the highly scored ones, we manage to remove spurious correlations and improve the overall performance by up to 6%, both in covariance and sub-population shift cases, both for a real and a synthetic benchmark, that we introduce for this task.

With growing attention to tabular data these days, the attempt to apply a synthetic table to various tasks has been expanded toward various scenarios. Owing to the recent advances in generative modeling, fake data generated by tabular data synthesis models become sophisticated and realistic. However, there still exists a difficulty in modeling discrete variables (columns) of tabular data. In this work, we propose to process continuous and discrete variables separately (but being conditioned on each other) by two diffusion models. The two diffusion models are co-evolved during training by reading conditions from each other. In order to further bind the diffusion models, moreover, we introduce a contrastive learning method with a negative sampling method. In our experiments with 11 real-world tabular datasets and 8 baseline methods, we prove the efficacy of the proposed method, called CoDi.

Modern deep networks are trained with stochastic gradient descent (SGD) whose key parameters are the number of data considered at each step or batch size $B$, and the step size or learning rate $\eta$. For small $B$ and large $\eta$, SGD corresponds to a stochastic evolution of the parameters, whose noise amplitude is governed by the `temperature' $T\equiv \eta/B$. Yet this description is observed to break down for sufficiently large batches $B\geq B^*$, or simplifies to gradient descent (GD) when the temperature is sufficiently small. Understanding where these cross-overs take place remains a central challenge. Here we resolve these questions for a teacher-student perceptron classification model, and show empirically that our key predictions still apply to deep networks. Specifically, we obtain a phase diagram in the $B$-$\eta$ plane that separates three dynamical phases: $\textit{(i)}$ a noise-dominated SGD governed by temperature, $\textit{(ii)}$ a large-first-step-dominated SGD and $\textit{(iii)}$ GD. These different phases also corresponds to different regimes of generalization error. Remarkably, our analysis reveals that the batch size $B^*$ separating regimes $\textit{(i)}$ and $\textit{(ii)}$ scale with the size $P$ of the training set, with an exponent that characterizes the hardness of the classification problem.

A variety of different performance metrics are commonly used in the machine learning literature for the evaluation of classification systems. Some of the most common ones for measuring quality of hard decisions are standard and balanced accuracy, standard and balanced error rate, F-beta score, and Matthews correlation coefficient (MCC). In this document, we review the definition of these and other metrics and compare them with the expected cost (EC), a metric introduced in every statistical learning course but rarely used in the machine learning literature. We show that both the standard and balanced error rates are special cases of the EC. Further, we show its relation with F-beta score and MCC and argue that EC is superior to these traditional metrics for being based on first principles from statistics, and for being more general, interpretable, and adaptable to any application scenario. The metrics mentioned above measure the quality of hard decisions. Yet, most modern classification systems output continuous scores for the classes which we may want to evaluate directly. Metrics for measuring the quality of system scores include the area under the ROC curve, equal error rate, cross-entropy, Brier score, and Bayes EC or Bayes risk, among others. The last three metrics are special cases of a family of metrics given by the expected value of proper scoring rules (PSRs). We review the theory behind these metrics, showing that they are a principled way to measure the quality of the posterior probabilities produced by a system. Finally, we show how to use these metrics to compute a system's calibration loss and compare this metric with the widely-used expected calibration error (ECE), arguing that calibration loss based on PSRs is superior to the ECE for being more interpretable, more general, and directly applicable to the multi-class case, among other reasons.

In open-world semi-supervised learning, a machine learning model is tasked with uncovering novel categories from unlabeled data while maintaining performance on seen categories from labeled data. The central challenge is the substantial learning gap between seen and novel categories, as the model learns the former faster due to accurate supervisory information. To address this, we introduce 1) an adaptive margin loss based on estimated class distribution, which encourages a large negative margin for samples in seen classes, to synchronize learning paces, and 2) pseudo-label contrastive clustering, which pulls together samples which are likely from the same class in the output space, to enhance novel class discovery. Our extensive evaluations on multiple datasets demonstrate that existing models still hinder novel class learning, whereas our approach strikingly balances both seen and novel classes, achieving a remarkable 3% average accuracy increase on the ImageNet dataset compared to the prior state-of-the-art. Additionally, we find that fine-tuning the self-supervised pre-trained backbone significantly boosts performance over the default in prior literature. After our paper is accepted, we will release the code.

Neural networks have become a prominent approach to solve inverse problems in recent years. While a plethora of such methods was developed to solve inverse problems empirically, we are still lacking clear theoretical guarantees for these methods. On the other hand, many works proved convergence to optimal solutions of neural networks in a more general setting using overparametrization as a way to control the Neural Tangent Kernel. In this work we investigate how to bridge these two worlds and we provide deterministic convergence and recovery guarantees for the class of unsupervised feedforward multilayer neural networks trained to solve inverse problems. We also derive overparametrization bounds under which a two-layers Deep Inverse Prior network with smooth activation function will benefit from our guarantees.

The problem of non-monotone $k$-submodular maximization under a knapsack constraint ($\kSMK$) over the ground set size $n$ has been raised in many applications in machine learning, such as data summarization, information propagation, etc. However, existing algorithms for the problem are facing questioning of how to overcome the non-monotone case and how to fast return a good solution in case of the big size of data. This paper introduces two deterministic approximation algorithms for the problem that competitively improve the query complexity of existing algorithms. Our first algorithm, $\LAA$, returns an approximation ratio of $1/19$ within $O(nk)$ query complexity. The second one, $\RLA$, improves the approximation ratio to $1/5-\epsilon$ in $O(nk)$ queries, where $\epsilon$ is an input parameter. Our algorithms are the first ones that provide constant approximation ratios within only $O(nk)$ query complexity for the non-monotone objective. They, therefore, need fewer the number of queries than state-of-the-the-art ones by a factor of $\Omega(\log n)$. Besides the theoretical analysis, we have evaluated our proposed ones with several experiments in some instances: Influence Maximization and Sensor Placement for the problem. The results confirm that our algorithms ensure theoretical quality as the cutting-edge techniques and significantly reduce the number of queries.

Disease progression simulation is a crucial area of research that has significant implications for clinical diagnosis, prognosis, and treatment. One major challenge in this field is the lack of continuous medical imaging monitoring of individual patients over time. To address this issue, we develop a novel framework termed Progressive Image Editing (PIE) that enables controlled manipulation of disease-related image features, facilitating precise and realistic disease progression simulation. Specifically, we leverage recent advancements in text-to-image generative models to simulate disease progression accurately and personalize it for each patient. We theoretically analyze the iterative refining process in our framework as a gradient descent with an exponentially decayed learning rate. To validate our framework, we conduct experiments in three medical imaging domains. Our results demonstrate the superiority of PIE over existing methods such as Stable Diffusion Walk and Style-Based Manifold Extrapolation based on CLIP score (Realism) and Disease Classification Confidence (Alignment). Our user study collected feedback from 35 veteran physicians to assess the generated progressions. Remarkably, 76.2% of the feedback agrees with the fidelity of the generated progressions. To our best knowledge, PIE is the first of its kind to generate disease progression images meeting real-world standards. It is a promising tool for medical research and clinical practice, potentially allowing healthcare providers to model disease trajectories over time, predict future treatment responses, and improve patient outcomes.

Federated Learning (FL) addresses the need to create models based on proprietary data in such a way that multiple clients retain exclusive control over their data, while all benefit from improved model accuracy due to pooled resources. Recently proposed Neural Graphical Models (NGMs) are Probabilistic Graphical models that utilize the expressive power of neural networks to learn complex non-linear dependencies between the input features. They learn to capture the underlying data distribution and have efficient algorithms for inference and sampling. We develop a FL framework which maintains a global NGM model that learns the averaged information from the local NGM models while keeping the training data within the client's environment. Our design, FedNGMs, avoids the pitfalls and shortcomings of neuron matching frameworks like Federated Matched Averaging that suffers from model parameter explosion. Our global model size remains constant throughout the process. In the cases where clients have local variables that are not part of the combined global distribution, we propose a `Stitching' algorithm, which personalizes the global NGM models by merging the additional variables using the client's data. FedNGM is robust to data heterogeneity, large number of participants, and limited communication bandwidth.

With the rapid development of big data and computing devices, low-latency automatic trading platforms based on real-time information acquisition have become the main components of the stock trading market, so the topic of quantitative trading has received widespread attention. And for non-strongly efficient trading markets, human emotions and expectations always dominate market trends and trading decisions. Therefore, this paper starts from the theory of emotion, taking East Money as an example, crawling user comment titles data from its corresponding stock bar and performing data cleaning. Subsequently, a natural language processing model BERT was constructed, and the BERT model was fine-tuned using existing annotated data sets. The experimental results show that the fine-tuned model has different degrees of performance improvement compared to the original model and the baseline model. Subsequently, based on the above model, the user comment data crawled is labeled with emotional polarity, and the obtained label information is combined with the Alpha191 model to participate in regression, and significant regression results are obtained. Subsequently, the regression model is used to predict the average price change for the next five days, and use it as a signal to guide automatic trading. The experimental results show that the incorporation of emotional factors increased the return rate by 73.8\% compared to the baseline during the trading period, and by 32.41\% compared to the original alpha191 model. Finally, we discuss the advantages and disadvantages of incorporating emotional factors into quantitative trading, and give possible directions for further research in the future.

This paper presents a Hierarchical Reinforcement Learning methodology tailored for optimizing CubeSat task scheduling in Low Earth Orbits (LEO). Incorporating a high-level policy for global task distribution and a low-level policy for real-time adaptations as a safety mechanism, our approach integrates the Similarity Attention-based Encoder (SABE) for task prioritization and an MLP estimator for energy consumption forecasting. Integrating this mechanism creates a safe and fault-tolerant system for CubeSat task scheduling. Simulation results validate the Hierarchical Reinforcement Learning superior convergence and task success rate, outperforming both the MADDPG model and traditional random scheduling across multiple CubeSat configurations.  ( 2 min )

Post-hoc explainability methods aim to clarify predictions of black-box machine learning models. However, it is still largely unclear how well users comprehend the provided explanations and whether these increase the users ability to predict the model behavior. We approach this question by conducting a user study to evaluate comprehensibility and predictability in two widely used tools: LIME and SHAP. Moreover, we investigate the effect of counterfactual explanations and misclassifications on users ability to understand and predict the model behavior. We find that the comprehensibility of SHAP is significantly reduced when explanations are provided for samples near a model's decision boundary. Furthermore, we find that counterfactual explanations and misclassifications can significantly increase the users understanding of how a machine learning model is making decisions. Based on our findings, we also derive design recommendations for future post-hoc explainability methods with increased comprehensibility and predictability.

Adiabatic Quantum-Flux-Parametron (AQFP) is a superconducting logic with extremely high energy efficiency. By employing the distinct polarity of current to denote logic `0' and `1', AQFP devices serve as excellent carriers for binary neural network (BNN) computations. Although recent research has made initial strides toward developing an AQFP-based BNN accelerator, several critical challenges remain, preventing the design from being a comprehensive solution. In this paper, we propose SupeRBNN, an AQFP-based randomized BNN acceleration framework that leverages software-hardware co-optimization to eventually make the AQFP devices a feasible solution for BNN acceleration. Specifically, we investigate the randomized behavior of the AQFP devices and analyze the impact of crossbar size on current attenuation, subsequently formulating the current amplitude into the values suitable for use in BNN computation. To tackle the accumulation problem and improve overall hardware performance, we propose a stochastic computing-based accumulation module and a clocking scheme adjustment-based circuit optimization method. We validate our SupeRBNN framework across various datasets and network architectures, comparing it with implementations based on different technologies, including CMOS, ReRAM, and superconducting RSFQ/ERSFQ. Experimental results demonstrate that our design achieves an energy efficiency of approximately 7.8x10^4 times higher than that of the ReRAM-based BNN framework while maintaining a similar level of model accuracy. Furthermore, when compared with superconductor-based counterparts, our framework demonstrates at least two orders of magnitude higher energy efficiency.  ( 3 min )

We introduce the Bittensor Language Model, called "BTLM-3B-8K", a new state-of-the-art 3 billion parameter open-source language model. BTLM-3B-8K was trained on 627B tokens from the SlimPajama dataset with a mixture of 2,048 and 8,192 context lengths. BTLM-3B-8K outperforms all existing 3B parameter models by 2-5.5% across downstream tasks. BTLM-3B-8K is even competitive with some 7B parameter models. Additionally, BTLM-3B-8K provides excellent long context performance, outperforming MPT-7B-8K and XGen-7B-8K on tasks up to 8,192 context length. We trained the model on a cleaned and deduplicated SlimPajama dataset; aggressively tuned the \textmu P hyperparameters and schedule; used ALiBi position embeddings; and adopted the SwiGLU nonlinearity. On Hugging Face, the most popular models have 7B parameters, indicating that users prefer the quality-size ratio of 7B models. Compacting the 7B parameter model to one with 3B parameters, with little performance impact, is an important milestone. BTLM-3B-8K needs only 3GB of memory with 4-bit precision and takes 2.5x less inference compute than 7B models, helping to open up access to a powerful language model on mobile and edge devices. BTLM-3B-8K is available under an Apache 2.0 license on Hugging Face: https://huggingface.co/cerebras/btlm-3b-8k-base.

In digital advertising, the selection of the optimal item (recommendation) and its best creative presentation (creative optimization) have traditionally been considered separate disciplines. However, both contribute significantly to user satisfaction, underpinning our assumption that it relies on both an item's relevance and its presentation, particularly in the case of visual creatives. In response, we introduce the task of {\itshape Generative Creative Optimization (GCO)}, which proposes the use of generative models for creative generation that incorporate user interests, and {\itshape AdBooster}, a model for personalized ad creatives based on the Stable Diffusion outpainting architecture. This model uniquely incorporates user interests both during fine-tuning and at generation time. To further improve AdBooster's performance, we also introduce an automated data augmentation pipeline. Through our experiments on simulated data, we validate AdBooster's effectiveness in generating more relevant creatives than default product images, showing its potential of enhancing user engagement.

A common formulation of constrained reinforcement learning involves multiple rewards that must individually accumulate to given thresholds. In this class of problems, we show a simple example in which the desired optimal policy cannot be induced by any weighted linear combination of rewards. Hence, there exist constrained reinforcement learning problems for which neither regularized nor classical primal-dual methods yield optimal policies. This work addresses this shortcoming by augmenting the state with Lagrange multipliers and reinterpreting primal-dual methods as the portion of the dynamics that drives the multipliers evolution. This approach provides a systematic state augmentation procedure that is guaranteed to solve reinforcement learning problems with constraints. Thus, as we illustrate by an example, while previous methods can fail at finding optimal policies, running the dual dynamics while executing the augmented policy yields an algorithm that provably samples actions from the optimal policy.  ( 2 min )

Simple regret minimization is a critical problem in learning optimal treatment assignment policies across various domains, including healthcare and e-commerce. However, it remains understudied in the contextual bandit setting. We propose a new family of computationally efficient bandit algorithms for the stochastic contextual bandit settings, with the flexibility to be adapted for cumulative regret minimization (with near-optimal minimax guarantees) and simple regret minimization (with SOTA guarantees). Furthermore, our algorithms adapt to model misspecification and extend to the continuous arm settings. These advantages come from constructing and relying on "conformal arm sets" (CASs), which provide a set of arms at every context that encompass the context-specific optimal arm with some probability across the context distribution. Our positive results on simple and cumulative regret guarantees are contrasted by a negative result, which shows that an algorithm can't achieve instance-dependent simple regret guarantees while simultaneously achieving minimax optimal cumulative regret guarantees.

In the analysis of optical coherence tomography angiography (OCTA) images, the operation of segmenting specific targets is necessary. Existing methods typically train on supervised datasets with limited samples (approximately a few hundred), which can lead to overfitting. To address this, the low-rank adaptation technique is adopted for foundation model fine-tuning and proposed corresponding prompt point generation strategies to process various segmentation tasks on OCTA datasets. This method is named SAM-OCTA and has been experimented on the publicly available OCTA-500 dataset. While achieving state-of-the-art performance metrics, this method accomplishes local vessel segmentation as well as effective artery-vein segmentation, which was not well-solved in previous works. The code is available at: https://github.com/ShellRedia/SAM-OCTA.

This study investigates how to schedule nanosatellite tasks more efficiently using Graph Neural Networks (GNNs). In the Offline Nanosatellite Task Scheduling (ONTS) problem, the goal is to find the optimal schedule for tasks to be carried out in orbit while taking into account Quality-of-Service (QoS) considerations such as priority, minimum and maximum activation events, execution time-frames, periods, and execution windows, as well as constraints on the satellite's power resources and the complexity of energy harvesting and management. The ONTS problem has been approached using conventional mathematical formulations and exact methods, but their applicability to challenging cases of the problem is limited. This study examines the use of GNNs in this context, which has been effectively applied to optimization problems such as the traveling salesman, scheduling, and facility placement problems. More specifically, we investigate whether GNNs can learn the complex structure of the ONTS problem with respect to feasibility and optimality of candidate solutions. Furthermore, we evaluate using GNN-based heuristic solutions to provide better solutions (w.r.t. the objective value) to the ONTS problem and reduce the optimization cost. Our experiments show that GNNs are not only able to learn feasibility and optimality for instances of the ONTS problem, but they can generalize to harder instances than those seen during training. Furthermore, the GNN-based heuristics improved the expected objective value of the best solution found under the time limit in 45%, and reduced the expected time to find a feasible solution in 35%, when compared to the SCIP (Solving Constraint Integer Programs) solver in its off-the-shelf configuration

Privacy-preserving crowd density analysis finds application across a wide range of scenarios, substantially enhancing smart building operation and management while upholding privacy expectations in various spaces. We propose a non-speech audio-based approach for crowd analytics, leveraging a transformer-based model. Our results demonstrate that non-speech audio alone can be used to conduct such analysis with remarkable accuracy. To the best of our knowledge, this is the first time when non-speech audio signals are proposed for predicting occupancy. As far as we know, there has been no other similar approach of its kind prior to this. To accomplish this, we deployed our sensor-based platform in the waiting room of a large hospital with IRB approval over a period of several months to capture non-speech audio and thermal images for the training and evaluation of our models. The proposed non-speech-based approach outperformed the thermal camera-based model and all other baselines. In addition to demonstrating superior performance without utilizing speech audio, we conduct further analysis using differential privacy techniques to provide additional privacy guarantees. Overall, our work demonstrates the viability of employing non-speech audio data for accurate occupancy estimation, while also ensuring the exclusion of speech-related content and providing robust privacy protections through differential privacy guarantees.

The idea of decision-aware model learning, that models should be accurate where it matters for decision-making, has gained prominence in model-based reinforcement learning. While promising theoretical results have been established, the empirical performance of algorithms leveraging a decision-aware loss has been lacking, especially in continuous control problems. In this paper, we present a study on the necessary components for decision-aware reinforcement learning models and we showcase design choices that enable well-performing algorithms. To this end, we provide a theoretical and empirical investigation into prominent algorithmic ideas in the field. We highlight that empirical design decisions established in the MuZero line of works are vital to achieving good performance for related algorithms, and we showcase differences in behavior between different instantiations of value-aware algorithms in stochastic environments. Using these insights, we propose the Latent Model-Based Decision-Aware Actor-Critic framework ($\lambda$-AC) for decision-aware model-based reinforcement learning in continuous state-spaces and highlight important design choices in different environments.

We study batched bandit experiments and consider the problem of inference conditional on the realized stopping time, assignment probabilities, and target parameter, where all of these may be chosen adaptively using information up to the last batch of the experiment. Absent further restrictions on the experiment, we show that inference using only the results of the last batch is optimal. When the adaptive aspects of the experiment are known to be location-invariant, in the sense that they are unchanged when we shift all batch-arm means by a constant, we show that there is additional information in the data, captured by one additional linear function of the batch-arm means. In the more restrictive case where the stopping time, assignment probabilities, and target parameter are known to depend on the data only through a collection of polyhedral events, we derive computationally tractable and optimal conditional inference procedures.  ( 2 min )

Presentation attack (spoofing) detection (PAD) typically operates alongside biometric verification to improve reliablity in the face of spoofing attacks. Even though the two sub-systems operate in tandem to solve the single task of reliable biometric verification, they address different detection tasks and are hence typically evaluated separately. Evidence shows that this approach is suboptimal. We introduce a new metric for the joint evaluation of PAD solutions operating in situ with biometric verification. In contrast to the tandem detection cost function proposed recently, the new tandem equal error rate (t-EER) is parameter free. The combination of two classifiers nonetheless leads to a \emph{set} of operating points at which false alarm and miss rates are equal and also dependent upon the prevalence of attacks. We therefore introduce the \emph{concurrent} t-EER, a unique operating point which is invariable to the prevalence of attacks. Using both modality (and even application) agnostic simulated scores, as well as real scores for a voice biometrics application, we demonstrate application of the t-EER to a wide range of biometric system evaluations under attack. The proposed approach is a strong candidate metric for the tandem evaluation of PAD systems and biometric comparators.

We present ForceSight, a system for text-guided mobile manipulation that predicts visual-force goals using a deep neural network. Given a single RGBD image combined with a text prompt, ForceSight determines a target end-effector pose in the camera frame (kinematic goal) and the associated forces (force goal). Together, these two components form a visual-force goal. Prior work has demonstrated that deep models outputting human-interpretable kinematic goals can enable dexterous manipulation by real robots. Forces are critical to manipulation, yet have typically been relegated to lower-level execution in these systems. When deployed on a mobile manipulator equipped with an eye-in-hand RGBD camera, ForceSight performed tasks such as precision grasps, drawer opening, and object handovers with an 81% success rate in unseen environments with object instances that differed significantly from the training data. In a separate experiment, relying exclusively on visual servoing and ignoring force goals dropped the success rate from 90% to 45%, demonstrating that force goals can significantly enhance performance. The appendix, videos, code, and trained models are available at https://force-sight.github.io/.

Objective: The objective of the study is to efficiently increase the expressivity of surface electromyography-based (sEMG) gesture recognition systems. Approach: We use a problem transformation approach, in which actions were subset into two biomechanically independent components - a set of wrist directions and a set of finger modifiers. To maintain fast calibration time, we train models for each component using only individual gestures, and extrapolate to the full product space of combination gestures by generating synthetic data. We collected a supervised dataset with high-confidence ground truth labels in which subjects performed combination gestures while holding a joystick, and conducted experiments to analyze the impact of model architectures, classifier algorithms, and synthetic data generation strategies on the performance of the proposed approach. Main Results: We found that a problem transformation approach using a parallel model architecture in combination with a non-linear classifier, along with restricted synthetic data generation, shows promise in increasing the expressivity of sEMG-based gestures with a short calibration time. Significance: sEMG-based gesture recognition has applications in human-computer interaction, virtual reality, and the control of robotic and prosthetic devices. Existing approaches require exhaustive model calibration. The proposed approach increases expressivity without requiring users to demonstrate all combination gesture classes. Our results may be extended to larger gesture vocabularies and more complicated model architectures.  ( 2 min )

Calibration measures and reliability diagrams are two fundamental tools for measuring and interpreting the calibration of probabilistic predictors. Calibration measures quantify the degree of miscalibration, and reliability diagrams visualize the structure of this miscalibration. However, the most common constructions of reliability diagrams and calibration measures -- binning and ECE -- both suffer from well-known flaws (e.g. discontinuity). We show that a simple modification fixes both constructions: first smooth the observations using an RBF kernel, then compute the Expected Calibration Error (ECE) of this smoothed function. We prove that with a careful choice of bandwidth, this method yields a calibration measure that is well-behaved in the sense of (B{\l}asiok, Gopalan, Hu, and Nakkiran 2023a) -- a consistent calibration measure. We call this measure the SmoothECE. Moreover, the reliability diagram obtained from this smoothed function visually encodes the SmoothECE, just as binned reliability diagrams encode the BinnedECE. We also provide a Python package with simple, hyperparameter-free methods for measuring and plotting calibration: `pip install relplot\`.

Despite the growing popularity of machine-learning techniques in decision-making, the added value of causal-oriented strategies with respect to pure machine-learning approaches has rarely been quantified in the literature. These strategies are crucial for practitioners in various domains, such as marketing, telecommunications, health care and finance. This paper presents a comprehensive treatment of the subject, starting from firm theoretical foundations and highlighting the parameters that influence the performance of the uplift and predictive approaches. The focus of the paper is on a binary outcome case and a binary action, and the paper presents a theoretical analysis of uplift modeling, comparing it with the classical predictive approach. The main research contributions of the paper include a new formulation of the measure of profit, a formal proof of the convergence of the uplift curve to the measure of profit ,and an illustration, through simulations, of the conditions under which predictive approaches still outperform uplift modeling. We show that the mutual information between the features and the outcome plays a significant role, along with the variance of the estimators, the distribution of the potential outcomes and the underlying costs and benefits of the treatment and the outcome.  ( 2 min )

Federated Learning (FL) has revolutionized how we train deep neural networks by enabling decentralized collaboration while safeguarding sensitive data and improving model performance. However, FL faces two crucial challenges: the diverse nature of data held by individual clients and the vulnerability of the FL system to security breaches. This paper introduces an innovative solution named Estimated Mean Aggregation (EMA) that not only addresses these challenges but also provides a fundamental reference point as a $\mathsf{baseline}$ for advanced aggregation techniques in FL systems. EMA's significance lies in its dual role: enhancing model security by effectively handling malicious outliers through trimmed means and uncovering data heterogeneity to ensure that trained models are adaptable across various client datasets. Through a wealth of experiments, EMA consistently demonstrates high accuracy and area under the curve (AUC) compared to alternative methods, establishing itself as a robust baseline for evaluating the effectiveness and security of FL aggregation methods. EMA's contributions thus offer a crucial step forward in advancing the efficiency, security, and versatility of decentralized deep learning in the context of FL.  ( 2 min )

We present LongLoRA, an efficient fine-tuning approach that extends the context sizes of pre-trained large language models (LLMs), with limited computation cost. Typically, training LLMs with long context sizes is computationally expensive, requiring extensive training hours and GPU resources. For example, training on the context length of 8192 needs 16x computational costs in self-attention layers as that of 2048. In this paper, we speed up the context extension of LLMs in two aspects. On the one hand, although dense global attention is needed during inference, fine-tuning the model can be effectively and efficiently done by sparse local attention. The proposed shift short attention effectively enables context extension, leading to non-trivial computation saving with similar performance to fine-tuning with vanilla attention. Particularly, it can be implemented with only two lines of code in training, while being optional in inference. On the other hand, we revisit the parameter-efficient fine-tuning regime for context expansion. Notably, we find that LoRA for context extension works well under the premise of trainable embedding and normalization. LongLoRA demonstrates strong empirical results on various tasks on LLaMA2 models from 7B/13B to 70B. LongLoRA adopts LLaMA2 7B from 4k context to 100k, or LLaMA2 70B to 32k on a single 8x A100 machine. LongLoRA extends models' context while retaining their original architectures, and is compatible with most existing techniques, like FlashAttention-2. In addition, to make LongLoRA practical, we collect a dataset, LongQA, for supervised fine-tuning. It contains more than 3k long context question-answer pairs.  ( 3 min )

Brain tumors are collections of abnormal cells that can develop into masses or clusters. Because they have the potential to infiltrate other tissues, they pose a risk to the patient. The main imaging technique used, MRI, may be able to identify a brain tumor with accuracy. The fast development of Deep Learning methods for use in computer vision applications has been facilitated by a vast amount of training data and improvements in model construction that offer better approximations in a supervised setting. The need for these approaches has been the main driver of this expansion. Deep learning methods have shown promise in improving the precision of brain tumor detection and classification using magnetic resonance imaging (MRI). The study on the use of deep learning techniques, especially ResNet50, for brain tumor identification is presented in this abstract. As a result, this study investigates the possibility of automating the detection procedure using deep learning techniques. In this study, I utilized five transfer learning models which are VGG16, VGG19, DenseNet121, ResNet50 and YOLO V4 where ResNet50 provide the best or highest accuracy 99.54%. The goal of the study is to guide researchers and medical professionals toward powerful brain tumor detecting systems by employing deep learning approaches by way of this evaluation and analysis.  ( 2 min )

In this work, we introduce Boolformer, the first Transformer architecture trained to perform end-to-end symbolic regression of Boolean functions. First, we show that it can predict compact formulas for complex functions which were not seen during training, when provided a clean truth table. Then, we demonstrate its ability to find approximate expressions when provided incomplete and noisy observations. We evaluate the Boolformer on a broad set of real-world binary classification datasets, demonstrating its potential as an interpretable alternative to classic machine learning methods. Finally, we apply it to the widespread task of modelling the dynamics of gene regulatory networks. Using a recent benchmark, we show that Boolformer is competitive with state-of-the art genetic algorithms with a speedup of several orders of magnitude. Our code and models are available publicly.  ( 2 min )

Transformer has been considered the dominating neural architecture in NLP and CV, mostly under supervised settings. Recently, a similar surge of using Transformers has appeared in the domain of reinforcement learning (RL), but it is faced with unique design choices and challenges brought by the nature of RL. However, the evolution of Transformers in RL has not yet been well unraveled. In this paper, we seek to systematically review motivations and progress on using Transformers in RL, provide a taxonomy on existing works, discuss each sub-field, and summarize future prospects.  ( 2 min )

The training of neural encoders via deep learning necessitates a differentiable channel model due to the backpropagation algorithm. This requirement can be sidestepped by approximating either the channel distribution or its gradient through pilot signals in real-world scenarios. The initial approach draws upon the latest advancements in image generation, utilizing generative adversarial networks (GANs) or their enhanced variants to generate channel distributions. In this paper, we address this channel approximation challenge with diffusion models, which have demonstrated high sample quality in image generation. We offer an end-to-end channel coding framework underpinned by diffusion models and propose an efficient training algorithm. Our simulations with various channel models establish that our diffusion models learn the channel distribution accurately, thereby achieving near-optimal end-to-end symbol error rates (SERs). We also note a significant advantage of diffusion models: A robust generalization capability in high signal-to-noise ratio regions, in contrast to GAN variants that suffer from error floor. Furthermore, we examine the trade-off between sample quality and sampling speed, when an accelerated sampling algorithm is deployed, and investigate the effect of the noise scheduling on this trade-off. With an apt choice of noise scheduling, sampling time can be significantly reduced with a minor increase in SER.  ( 2 min )

For cyber-physical systems in the 6G era, semantic communications connecting distributed devices for dynamic control and remote state estimation are required to guarantee application-level performance, not merely focus on communication-centric performance. Semantics here is a measure of the usefulness of information transmissions. Semantic-aware transmission scheduling of a large system often involves a large decision-making space, and the optimal policy cannot be obtained by existing algorithms effectively. In this paper, we first investigate the fundamental properties of the optimal semantic-aware scheduling policy and then develop advanced deep reinforcement learning (DRL) algorithms by leveraging the theoretical guidelines. Our numerical results show that the proposed algorithms can substantially reduce training time and enhance training performance compared to benchmark algorithms.

Autistic Spectrum Disorder (ASD) is a neurological disease characterized by difficulties with social interaction, communication, and repetitive activities. The severity of these difficulties varies, and those with this diagnosis face unique challenges. While its primary origin lies in genetics, identifying and addressing it early can contribute to the enhancement of the condition. In recent years, machine learning-driven intelligent diagnosis has emerged as a supplement to conventional clinical approaches, aiming to address the potential drawbacks of time-consuming and costly traditional methods. In this work, we utilize different machine learning algorithms to find the most significant traits responsible for ASD and to automate the diagnostic process. We study six classification models to see which model works best to identify ASD and also study five popular clustering methods to get a meaningful insight of these ASD datasets. To find the best classifier for these binary datasets, we evaluate the models using accuracy, precision, recall, specificity, F1-score, AUC, kappa and log loss metrics. Our evaluation demonstrates that five out of the six selected models perform exceptionally, achieving a 100% accuracy rate on the ASD datasets when hyperparameters are meticulously tuned for each model. As almost all classification models are able to get 100% accuracy, we become interested in observing the underlying insights of the datasets by implementing some popular clustering algorithms on these datasets. We calculate Normalized Mutual Information (NMI), Adjusted Rand Index (ARI) & Silhouette Coefficient (SC) metrics to select the best clustering models. Our evaluation finds that spectral clustering outperforms all other benchmarking clustering models in terms of NMI & ARI metrics and it also demonstrates comparability to the optimal SC achieved by k-means.

Recently, studies on machine learning have focused on methods that use symmetry implicit in a specific manifold as an inductive bias. Grassmann manifolds provide the ability to handle fundamental shapes represented as shape spaces, enabling stable shape analysis. In this paper, we present a novel approach in which we establish the theoretical foundations for learning distributions on the Grassmann manifold via continuous normalization flows, with the explicit goal of generating stable shapes. Our approach facilitates more robust generation by effectively eliminating the influence of extraneous transformations, such as rotations and inversions, through learning and generating within a Grassmann manifolds designed to accommodate the essential shape information of the object. The experimental results indicated that the proposed method can generate high-quality samples by capturing the data structure. Furthermore, the proposed method significantly outperformed state-of-the-art methods in terms of the log-likelihood or evidence lower bound. The results obtained are expected to stimulate further research in this field, leading to advances for stable shape generation and analysis.

This paper studies the problem of traffic flow forecasting, which aims to predict future traffic conditions on the basis of road networks and traffic conditions in the past. The problem is typically solved by modeling complex spatio-temporal correlations in traffic data using spatio-temporal graph neural networks (GNNs). However, the performance of these methods is still far from satisfactory since GNNs usually have limited representation capacity when it comes to complex traffic networks. Graphs, by nature, fall short in capturing non-pairwise relations. Even worse, existing methods follow the paradigm of message passing that aggregates neighborhood information linearly, which fails to capture complicated spatio-temporal high-order interactions. To tackle these issues, in this paper, we propose a novel model named Dynamic Hypergraph Structure Learning (DyHSL) for traffic flow prediction. To learn non-pairwise relationships, our DyHSL extracts hypergraph structural information to model dynamics in the traffic networks, and updates each node representation by aggregating messages from its associated hyperedges. Additionally, to capture high-order spatio-temporal relations in the road network, we introduce an interactive graph convolution block, which further models the neighborhood interaction for each node. Finally, we integrate these two views into a holistic multi-scale correlation extraction module, which conducts temporal pooling with different scales to model different temporal patterns. Extensive experiments on four popular traffic benchmark datasets demonstrate the effectiveness of our proposed DyHSL compared with a broad range of competing baselines.  ( 3 min )

Linking sheet music images to audio recordings remains a key problem for the development of efficient cross-modal music retrieval systems. One of the fundamental approaches toward this task is to learn a cross-modal embedding space via deep neural networks that is able to connect short snippets of audio and sheet music. However, the scarcity of annotated data from real musical content affects the capability of such methods to generalize to real retrieval scenarios. In this work, we investigate whether we can mitigate this limitation with self-supervised contrastive learning, by exposing a network to a large amount of real music data as a pre-training step, by contrasting randomly augmented views of snippets of both modalities, namely audio and sheet images. Through a number of experiments on synthetic and real piano data, we show that pre-trained models are able to retrieve snippets with better precision in all scenarios and pre-training configurations. Encouraged by these results, we employ the snippet embeddings in the higher-level task of cross-modal piece identification and conduct more experiments on several retrieval configurations. In this task, we observe that the retrieval quality improves from 30% up to 100% when real music data is present. We then conclude by arguing for the potential of self-supervised contrastive learning for alleviating the annotated data scarcity in multi-modal music retrieval models.

The rapid development of 3D object detection systems for self-driving cars has significantly improved accuracy. However, these systems struggle to generalize across diverse driving environments, which can lead to safety-critical failures in detecting traffic participants. To address this, we propose a method that utilizes unlabeled repeated traversals of multiple locations to adapt object detectors to new driving environments. By incorporating statistics computed from repeated LiDAR scans, we guide the adaptation process effectively. Our approach enhances LiDAR-based detection models using spatial quantized historical features and introduces a lightweight regression head to leverage the statistics for feature regularization. Additionally, we leverage the statistics for a novel self-training process to stabilize the training. The framework is detector model-agnostic and experiments on real-world datasets demonstrate significant improvements, achieving up to a 20-point performance gain, especially in detecting pedestrians and distant objects. Code is available at https://github.com/zhangtravis/Hist-DA.

Quantum machine learning is arguably one of the most explored applications of near-term quantum devices. Much focus has been put on notions of variational quantum machine learning where parameterized quantum circuits (PQCs) are used as learning models. These PQC models have a rich structure which suggests that they might be amenable to efficient dequantization via random Fourier features (RFF). In this work, we establish necessary and sufficient conditions under which RFF does indeed provide an efficient dequantization of variational quantum machine learning for regression. We build on these insights to make concrete suggestions for PQC architecture design, and to identify structures which are necessary for a regression problem to admit a potential quantum advantage via PQC based optimization.

Audiovisual data is everywhere in this digital age, which raises higher requirements for the deep learning models developed on them. To well handle the information of the multi-modal data is the key to a better audiovisual modal. We observe that these audiovisual data naturally have temporal attributes, such as the time information for each frame in the video. More concretely, such data is inherently multi-modal according to both audio and visual cues, which proceed in a strict chronological order. It indicates that temporal information is important in multi-modal acoustic event modeling for both intra- and inter-modal. However, existing methods deal with each modal feature independently and simply fuse them together, which neglects the mining of temporal relation and thus leads to sub-optimal performance. With this motivation, we propose a Temporal Multi-modal graph learning method for Acoustic event Classification, called TMac, by modeling such temporal information via graph learning techniques. In particular, we construct a temporal graph for each acoustic event, dividing its audio data and video data into multiple segments. Each segment can be considered as a node, and the temporal relationships between nodes can be considered as timestamps on their edges. In this case, we can smoothly capture the dynamic information in intra-modal and inter-modal. Several experiments are conducted to demonstrate TMac outperforms other SOTA models in performance. Our code is available at https://github.com/MGitHubL/TMac.

Anticancer peptides (ACPs) are a group of peptides that exhibite antineoplastic properties. The utilization of ACPs in cancer prevention can present a viable substitute for conventional cancer therapeutics, as they possess a higher degree of selectivity and safety. Recent scientific advancements generate an interest in peptide-based therapies which offer the advantage of efficiently treating intended cells without negatively impacting normal cells. However, as the number of peptide sequences continues to increase rapidly, developing a reliable and precise prediction model becomes a challenging task. In this work, our motivation is to advance an efficient model for categorizing anticancer peptides employing the consolidation of word embedding and deep learning models. First, Word2Vec and FastText are evaluated as word embedding techniques for the purpose of extracting peptide sequences. Then, the output of word embedding models are fed into deep learning approaches CNN, LSTM, BiLSTM. To demonstrate the contribution of proposed framework, extensive experiments are carried on widely-used datasets in the literature, ACPs250 and Independent. Experiment results show the usage of proposed model enhances classification accuracy when compared to the state-of-the-art studies. The proposed combination, FastText+BiLSTM, exhibits 92.50% of accuracy for ACPs250 dataset, and 96.15% of accuracy for Independent dataset, thence determining new state-of-the-art.

We formulate a data independent latent space regularisation constraint for general unsupervised autoencoders. The regularisation rests on sampling the autoencoder Jacobian in Legendre nodes, being the centre of the Gauss-Legendre quadrature. Revisiting this classic enables to prove that regularised autoencoders ensure a one-to-one re-embedding of the initial data manifold to its latent representation. Demonstrations show that prior proposed regularisation strategies, such as contractive autoencoding, cause topological defects already for simple examples, and so do convolutional based (variational) autoencoders. In contrast, topological preservation is ensured already by standard multilayer perceptron neural networks when being regularised due to our contribution. This observation extends through the classic FashionMNIST dataset up to real world encoding problems for MRI brain scans, suggesting that, across disciplines, reliable low dimensional representations of complex high-dimensional datasets can be delivered due to this regularisation technique.  ( 2 min )

Indoor localization is getting increasing demands for various cutting-edged technologies, like Virtual/Augmented reality and smart home. Traditional model-based localization suffers from significant computational overhead, so fingerprint localization is getting increasing attention, which needs lower computation cost after the fingerprint database is built. However, the accuracy of indoor localization is limited by the complicated indoor environment which brings the multipath signal refraction. In this paper, we provided a scheme to improve the accuracy of indoor fingerprint localization from the frequency domain by predicting the channel state information (CSI) values from another transmitting channel and spliced the multi-band information together to get more precise localization results. We tested our proposed scheme on COST 2100 simulation data and real time orthogonal frequency division multiplexing (OFDM) WiFi data collected from an office scenario.  ( 2 min )

Human drivers can seamlessly adapt their driving decisions across geographical locations with diverse conditions and rules of the road, e.g., left vs. right-hand traffic. In contrast, existing models for autonomous driving have been thus far only deployed within restricted operational domains, i.e., without accounting for varying driving behaviors across locations or model scalability. In this work, we propose AnyD, a single geographically-aware conditional imitation learning (CIL) model that can efficiently learn from heterogeneous and globally distributed data with dynamic environmental, traffic, and social characteristics. Our key insight is to introduce a high-capacity geo-location-based channel attention mechanism that effectively adapts to local nuances while also flexibly modeling similarities among regions in a data-driven manner. By optimizing a contrastive imitation objective, our proposed approach can efficiently scale across inherently imbalanced data distributions and location-dependent events. We demonstrate the benefits of our AnyD agent across multiple datasets, cities, and scalable deployment paradigms, i.e., centralized, semi-supervised, and distributed agent training. Specifically, AnyD outperforms CIL baselines by over 14% in open-loop evaluation and 30% in closed-loop testing on CARLA.  ( 2 min )

Deep learning's immense capabilities are often constrained by the complexity of its models, leading to an increasing demand for effective sparsification techniques. Bayesian sparsification for deep learning emerges as a crucial approach, facilitating the design of models that are both computationally efficient and competitive in terms of performance across various deep learning applications. The state-of-the-art -- in Bayesian sparsification of deep neural networks -- combines structural shrinkage priors on model weights with an approximate inference scheme based on black-box stochastic variational inference. However, model inversion of the full generative model is exceptionally computationally demanding, especially when compared to standard deep learning of point estimates. In this context, we advocate for the use of Bayesian model reduction (BMR) as a more efficient alternative for pruning of model weights. As a generalization of the Savage-Dickey ratio, BMR allows a post-hoc elimination of redundant model weights based on the posterior estimates under a straightforward (non-hierarchical) generative model. Our comparative study highlights the computational efficiency and the pruning rate of the BMR method relative to the established stochastic variational inference (SVI) scheme, when applied to the full hierarchical generative model. We illustrate the potential of BMR to prune model parameters across various deep learning architectures, from classical networks like LeNet to modern frameworks such as Vision Transformers and MLP-Mixers.

Consider an online convex optimization problem where the loss functions are self-concordant barriers, smooth relative to a convex function $h$, and possibly non-Lipschitz. We analyze the regret of online mirror descent with $h$. Then, based on the result, we prove the following in a unified manner. Denote by $T$ the time horizon and $d$ the parameter dimension. 1. For online portfolio selection, the regret of $\widetilde{\text{EG}}$, a variant of exponentiated gradient due to Helmbold et al., is $\tilde{O} ( T^{2/3} d^{1/3} )$ when $T > 4 d / \log d$. This improves on the original $\tilde{O} ( T^{3/4} d^{1/2} )$ regret bound for $\widetilde{\text{EG}}$. 2. For online portfolio selection, the regret of online mirror descent with the logarithmic barrier is $\tilde{O}(\sqrt{T d})$. The regret bound is the same as that of Soft-Bayes due to Orseau et al. up to logarithmic terms. 3. For online learning quantum states with the logarithmic loss, the regret of online mirror descent with the log-determinant function is also $\tilde{O} ( \sqrt{T d} )$. Its per-iteration time is shorter than all existing algorithms we know.  ( 3 min )

Federated training of Graph Neural Networks (GNN) has become popular in recent years due to its ability to perform graph-related tasks under data isolation scenarios while preserving data privacy. However, graph heterogeneity issues in federated GNN systems continue to pose challenges. Existing frameworks address the problem by representing local tasks using different statistics and relating them through a simple aggregation mechanism. However, these approaches suffer from limited efficiency from two aspects: low quality of task-relatedness quantification and inefficacy of exploiting the collaboration structure. To address these issues, we propose FedGKD, a novel federated GNN framework that utilizes a novel client-side graph dataset distillation method to extract task features that better describe task-relatedness, and introduces a novel server-side aggregation mechanism that is aware of the global collaboration structure. We conduct extensive experiments on six real-world datasets of different scales, demonstrating our framework's outperformance.

Surrogate-assisted evolutionary algorithms (SAEAs) hold significant importance in resolving expensive optimization problems~(EOPs). Extensive efforts have been devoted to improving the efficacy of SAEAs through the development of proficient model-assisted selection methods. However, generating high-quality solutions is a prerequisite for selection. The fundamental paradigm of evaluating a limited number of solutions in each generation within SAEAs reduces the variance of adjacent populations, thus impacting the quality of offspring solutions. This is a frequently encountered issue, yet it has not gained widespread attention. This paper presents a framework using unevaluated solutions to enhance the efficiency of SAEAs. The surrogate model is employed to identify high-quality solutions for direct generation of new solutions without evaluation. To ensure dependable selection, we have introduced two tailored relation models for the selection of the optimal solution and the unevaluated population. A comprehensive experimental analysis is performed on two test suites, which showcases the superiority of the relation model over regression and classification models in the selection phase. Furthermore, the surrogate-selected unevaluated solutions with high potential have been shown to significantly enhance the efficiency of the algorithm.

Evaluation of QA systems is very challenging and expensive, with the most reliable approach being human annotations of correctness of answers for questions. Recent works (AVA, BEM) have shown that transformer LM encoder based similarity metrics transfer well for QA evaluation, but they are limited by the usage of a single correct reference answer. We propose a new evaluation metric: SQuArE (Sentence-level QUestion AnsweRing Evaluation), using multiple reference answers (combining multiple correct and incorrect references) for sentence-form QA. We evaluate SQuArE on both sentence-level extractive (Answer Selection) and generative (GenQA) QA systems, across multiple academic and industrial datasets, and show that it outperforms previous baselines and obtains the highest correlation with human annotations.

Scene flow estimation is the task of describing the 3D motion field between temporally successive point clouds. State-of-the-art methods use strong priors and test-time optimization techniques, but require on the order of tens of seconds to process large-scale point clouds, making them unusable as computer vision primitives for real-time applications such as open world object detection. Feed forward methods are considerably faster, running on the order of tens to hundreds of milliseconds for large-scale point clouds, but require expensive human supervision. To address both limitations, we propose Scene Flow via Distillation, a simple, scalable distillation framework that uses a label-free optimization method to produce pseudo-labels to supervise a feed forward model. Our instantiation of this framework, ZeroFlow, achieves state-of-the-art performance on the Argoverse 2 Self-Supervised Scene Flow Challenge while using zero human labels by simply training on large-scale, diverse unlabeled data. At test-time, ZeroFlow is over 1000$\times$ faster than label-free state-of-the-art optimization-based methods on large-scale point clouds and over 1000$\times$ cheaper to train on unlabeled data compared to the cost of human annotation of that data. To facilitate further research, we will release our code, trained model weights, and high quality pseudo-labels for the Argoverse 2 and Waymo Open datasets.

Recently, large multimodal models, such as CLIP and Stable Diffusion have experimented tremendous successes in both foundations and applications. However, as these models increase in parameter size and computational requirements, it becomes more challenging for users to personalize them for specific tasks or preferences. In this work, we address the problem of adapting the previous models towards sets of particular human preferences, aligning the retrieved or generated images with the preferences of the user. We leverage the Bradley-Terry preference model to develop a fast adaptation method that efficiently fine-tunes the original model, with few examples and with minimal computing resources. Extensive evidence of the capabilities of this framework is provided through experiments in different domains related to multimodal text and image understanding, including preference prediction as a reward model, and generation tasks.

Equipping multi-fingered robots with tactile sensing is crucial for achieving the precise, contact-rich, and dexterous manipulation that humans excel at. However, relying solely on tactile sensing fails to provide adequate cues for reasoning about objects' spatial configurations, limiting the ability to correct errors and adapt to changing situations. In this paper, we present Tactile Adaptation from Visual Incentives (TAVI), a new framework that enhances tactile-based dexterity by optimizing dexterous policies using vision-based rewards. First, we use a contrastive-based objective to learn visual representations. Next, we construct a reward function using these visual representations through optimal-transport based matching on one human demonstration. Finally, we use online reinforcement learning on our robot to optimize tactile-based policies that maximize the visual reward. On six challenging tasks, such as peg pick-and-place, unstacking bowls, and flipping slender objects, TAVI achieves a success rate of 73% using our four-fingered Allegro robot hand. The increase in performance is 108% higher than policies using tactile and vision-based rewards and 135% higher than policies without tactile observational input. Robot videos are best viewed on our project website: https://see-to-touch.github.io/.

Generating multi-instrument music from symbolic music representations is an important task in Music Information Retrieval (MIR). A central but still largely unsolved problem in this context is musically and acoustically informed control in the generation process. As the main contribution of this work, we propose enhancing control of multi-instrument synthesis by conditioning a generative model on a specific performance and recording environment, thus allowing for better guidance of timbre and style. Building on state-of-the-art diffusion-based music generative models, we introduce performance conditioning - a simple tool indicating the generative model to synthesize music with style and timbre of specific instruments taken from specific performances. Our prototype is evaluated using uncurated performances with diverse instrumentation and achieves state-of-the-art FAD realism scores while allowing novel timbre and style control. Our project page, including samples and demonstrations, is available at benadar293.github.io/midipm

Ensuring that analyses performed on a dataset are representative of the entire population is one of the central problems in statistics. Most classical techniques assume that the dataset is independent of the analyst's query and break down in the common setting where a dataset is reused for multiple, adaptively chosen, queries. This problem of \emph{adaptive data analysis} was formalized in the seminal works of Dwork et al. (STOC, 2015) and Hardt and Ullman (FOCS, 2014). We identify a remarkably simple set of assumptions under which the queries will continue to be representative even when chosen adaptively: The only requirements are that each query takes as input a random subsample and outputs few bits. This result shows that the noise inherent in subsampling is sufficient to guarantee that query responses generalize. The simplicity of this subsampling-based framework allows it to model a variety of real-world scenarios not covered by prior work. In addition to its simplicity, we demonstrate the utility of this framework by designing mechanisms for two foundational tasks, statistical queries and median finding. In particular, our mechanism for answering the broadly applicable class of statistical queries is both extremely simple and state of the art in many parameter regimes.

We demonstrate the first algorithms for the problem of regression for generalized linear models (GLMs) in the presence of additive oblivious noise. We assume we have sample access to examples $(x, y)$ where $y$ is a noisy measurement of $g(w^* \cdot x)$. In particular, \new{the noisy labels are of the form} $y = g(w^* \cdot x) + \xi + \epsilon$, where $\xi$ is the oblivious noise drawn independently of $x$ \new{and satisfies} $\Pr[\xi = 0] \geq o(1)$, and $\epsilon \sim \mathcal N(0, \sigma^2)$. Our goal is to accurately recover a \new{parameter vector $w$ such that the} function $g(w \cdot x)$ \new{has} arbitrarily small error when compared to the true values $g(w^* \cdot x)$, rather than the noisy measurements $y$. We present an algorithm that tackles \new{this} problem in its most general distribution-independent setting, where the solution may not \new{even} be identifiable. \new{Our} algorithm returns \new{an accurate estimate of} the solution if it is identifiable, and otherwise returns a small list of candidates, one of which is close to the true solution. Furthermore, we \new{provide} a necessary and sufficient condition for identifiability, which holds in broad settings. \new{Specifically,} the problem is identifiable when the quantile at which $\xi + \epsilon = 0$ is known, or when the family of hypotheses does not contain candidates that are nearly equal to a translated $g(w^* \cdot x) + A$ for some real number $A$, while also having large error when compared to $g(w^* \cdot x)$. This is the first \new{algorithmic} result for GLM regression \new{with oblivious noise} which can handle more than half the samples being arbitrarily corrupted. Prior work focused largely on the setting of linear regression, and gave algorithms under restrictive assumptions.

Privacy-preserving learning of gradient boosting decision trees (GBDT) has the potential for strong utility-privacy tradeoffs for tabular data, such as census data or medical meta data: classical GBDT learners can extract non-linear patterns from small sized datasets. The state-of-the-art notion for provable privacy-properties is differential privacy, which requires that the impact of single data points is limited and deniable. We introduce a novel differentially private GBDT learner and utilize four main techniques to improve the utility-privacy tradeoff. (1) We use an improved noise scaling approach with tighter accounting of privacy leakage of a decision tree leaf compared to prior work, resulting in noise that in expectation scales with $O(1/n)$, for $n$ data points. (2) We integrate individual R\'enyi filters to our method to learn from data points that have been underutilized during an iterative training process, which -- potentially of independent interest -- results in a natural yet effective insight to learning streams of non-i.i.d. data. (3) We incorporate the concept of random decision tree splits to concentrate privacy budget on learning leaves. (4) We deploy subsampling for privacy amplification. Our evaluation shows for the Abalone dataset ($<4k$ training data points) a $R^2$-score of $0.39$ for $\varepsilon=0.15$, which the closest prior work only achieved for $\varepsilon=10.0$. On the Adult dataset ($50k$ training data points) we achieve test error of $18.7\,\%$ for $\varepsilon=0.07$ which the closest prior work only achieved for $\varepsilon=1.0$. For the Abalone dataset for $\varepsilon=0.54$ we achieve $R^2$-score of $0.47$ which is very close to the $R^2$-score of $0.54$ for the nonprivate version of GBDT. For the Adult dataset for $\varepsilon=0.54$ we achieve test error $17.1\,\%$ which is very close to the test error $13.7\,\%$ of the nonprivate version of GBDT.

Virtual screening of large compound libraries to identify potential hit candidates is one of the earliest steps in drug discovery. As the size of commercially available compound collections grows exponentially to the scale of billions, brute-force virtual screening using traditional tools such as docking becomes infeasible in terms of time and computational resources. Active learning and Bayesian optimization has recently been proven as effective methods of narrowing down the search space. An essential component in those methods is a surrogate machine learning model that is trained with a small subset of the library to predict the desired properties of compounds. Accurate model can achieve high sample efficiency by finding the most promising compounds with only a fraction of the whole library being virtually screened. In this study, we examined the performance of pretrained transformer-based language model and graph neural network in Bayesian optimization active learning framework. The best pretrained models identifies 58.97% of the top-50000 by docking score after screening only 0.6% of an ultra-large library containing 99.5 million compounds, improving 8% over previous state-of-the-art baseline. Through extensive benchmarks, we show that the superior performance of pretrained models persists in both structure-based and ligand-based drug discovery. Such model can serve as a boost to the accuracy and sample efficiency of active learning based molecule virtual screening.

We present a novel adversarial model for authentication systems that use gait patterns recorded by the inertial measurement unit (IMU) built into smartphones. The attack idea is inspired by and named after the concept of a dictionary attack on knowledge (PIN or password) based authentication systems. In particular, this work investigates whether it is possible to build a dictionary of IMUGait patterns and use it to launch an attack or find an imitator who can actively reproduce IMUGait patterns that match the target's IMUGait pattern. Nine physically and demographically diverse individuals walked at various levels of four predefined controllable and adaptable gait factors (speed, step length, step width, and thigh-lift), producing 178 unique IMUGait patterns. Each pattern attacked a wide variety of user authentication models. The deeper analysis of error rates (before and after the attack) challenges the belief that authentication systems based on IMUGait patterns are the most difficult to spoof; further research is needed on adversarial models and associated countermeasures.

Text-conditioned image generation models have recently achieved astonishing image quality and alignment results. Consequently, they are employed in a fast-growing number of applications. Since they are highly data-driven, relying on billion-sized datasets randomly scraped from the web, they also produce unsafe content. As a contribution to the Adversarial Nibbler challenge, we distill a large set of over 1,000 potential adversarial inputs from existing safety benchmarks. Our analysis of the gathered prompts and corresponding images demonstrates the fragility of input filters and provides further insights into systematic safety issues in current generative image models.

Accurate load forecasting plays a vital role in numerous sectors, but accurately capturing the complex dynamics of dynamic power systems remains a challenge for traditional statistical models. For these reasons, time-series models (ARIMA) and deep-learning models (ANN, LSTM, GRU, etc.) are commonly deployed and often experience higher success. In this paper, we analyze the efficacy of the recently developed Transformer-based Neural Network model in Load forecasting. Transformer models have the potential to improve Load forecasting because of their ability to learn long-range dependencies derived from their Attention Mechanism. We apply several metaheuristics namely Differential Evolution to find the optimal hyperparameters of the Transformer-based Neural Network to produce accurate forecasts. Differential Evolution provides scalable, robust, global solutions to non-differentiable, multi-objective, or constrained optimization problems. Our work compares the proposed Transformer based Neural Network model integrated with different metaheuristic algorithms by their performance in Load forecasting based on numerical metrics such as Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). Our findings demonstrate the potential of metaheuristic-enhanced Transformer-based Neural Network models in Load forecasting accuracy and provide optimal hyperparameters for each model.

Out-of-distribution (OOD) detection is crucial to modern deep learning applications by identifying and alerting about the OOD samples that should not be tested or used for making predictions. Current OOD detection methods have made significant progress when in-distribution (ID) and OOD samples are drawn from static distributions. However, this can be unrealistic when applied to real-world systems which often undergo continuous variations and shifts in ID and OOD distributions over time. Therefore, for an effective application in real-world systems, the development of OOD detection methods that can adapt to these dynamic and evolving distributions is essential. In this paper, we propose a novel and more realistic setting called continuously adaptive out-of-distribution (CAOOD) detection which targets on developing an OOD detection model that enables dynamic and quick adaptation to a new arriving distribution, with insufficient ID samples during deployment time. To address CAOOD, we develop meta OOD learning (MOL) by designing a learning-to-adapt diagram such that a good initialized OOD detection model is learned during the training process. In the testing process, MOL ensures OOD detection performance over shifting distributions by quickly adapting to new distributions with a few adaptations. Extensive experiments on several OOD benchmarks endorse the effectiveness of our method in preserving both ID classification accuracy and OOD detection performance on continuously shifting distributions.

We expose a surprising failure of generalization in auto-regressive large language models (LLMs). If a model is trained on a sentence of the form "A is B", it will not automatically generalize to the reverse direction "B is A". This is the Reversal Curse. For instance, if a model is trained on "Olaf Scholz was the ninth Chancellor of Germany", it will not automatically be able to answer the question, "Who was the ninth Chancellor of Germany?". Moreover, the likelihood of the correct answer ("Olaf Scholz") will not be higher than for a random name. Thus, models exhibit a basic failure of logical deduction and do not generalize a prevalent pattern in their training set (i.e. if "A is B'' occurs, "B is A" is more likely to occur). We provide evidence for the Reversal Curse by finetuning GPT-3 and Llama-1 on fictitious statements such as "Uriah Hawthorne is the composer of 'Abyssal Melodies'" and showing that they fail to correctly answer "Who composed 'Abyssal Melodies?'". The Reversal Curse is robust across model sizes and model families and is not alleviated by data augmentation. We also evaluate ChatGPT (GPT-3.5 and GPT-4) on questions about real-world celebrities, such as "Who is Tom Cruise's mother? [A: Mary Lee Pfeiffer]" and the reverse "Who is Mary Lee Pfeiffer's son?". GPT-4 correctly answers questions like the former 79% of the time, compared to 33% for the latter. This shows a failure of logical deduction that we hypothesize is caused by the Reversal Curse. Code is available at https://github.com/lukasberglund/reversal_curse.

Graph Neural Networks (GNNs) have achieved tremendous success in a variety of real-world applications by relying on the fixed graph data as input. However, the initial input graph might not be optimal in terms of specific downstream tasks, because of information scarcity, noise, adversarial attacks, or discrepancies between the distribution in graph topology, features, and groundtruth labels. In this paper, we propose a bi-level optimization approach for learning the optimal graph structure via directly learning the Personalized PageRank propagation matrix as well as the downstream semi-supervised node classification simultaneously. We also explore a low-rank approximation model for further reducing the time complexity. Empirical evaluations show the superior efficacy and robustness of the proposed model over all baseline methods.

In recent years, datasets of paired audio and captions have enabled remarkable success in automatically generating descriptions for audio clips, namely Automated Audio Captioning (AAC). However, it is labor-intensive and time-consuming to collect a sufficient number of paired audio and captions. Motivated by the recent advances in Contrastive Language-Audio Pretraining (CLAP), we propose a weakly-supervised approach to train an AAC model assuming only text data and a pre-trained CLAP model, alleviating the need for paired target data. Our approach leverages the similarity between audio and text embeddings in CLAP. During training, we learn to reconstruct the text from the CLAP text embedding, and during inference, we decode using the audio embeddings. To mitigate the modality gap between the audio and text embeddings we employ strategies to bridge the gap during training and inference stages. We evaluate our proposed method on Clotho and AudioCaps datasets demonstrating its ability to achieve a relative performance of up to ~$83\%$ compared to fully supervised approaches trained with paired target data.

Structure learning is the crux of causal inference. Notably, causal discovery (CD) algorithms are brittle when data is scarce, possibly inferring imprecise causal relations that contradict expert knowledge -- especially when considering latent confounders. To aggravate the issue, most CD methods do not provide uncertainty estimates, making it hard for users to interpret results and improve the inference process. Surprisingly, while CD is a human-centered affair, no works have focused on building methods that both 1) output uncertainty estimates that can be verified by experts and 2) interact with those experts to iteratively refine CD. To solve these issues, we start by proposing to sample (causal) ancestral graphs proportionally to a belief distribution based on a score function, such as the Bayesian information criterion (BIC), using generative flow networks. Then, we leverage the diversity in candidate graphs and introduce an optimal experimental design to iteratively probe the expert about the relations among variables, effectively reducing the uncertainty of our belief over ancestral graphs. Finally, we update our samples to incorporate human feedback via importance sampling. Importantly, our method does not require causal sufficiency (i.e., unobserved confounders may exist). Experiments with synthetic observational data show that our method can accurately sample from distributions over ancestral graphs and that we can greatly improve inference quality with human aid.

We consider the problem of learning multiple tasks in a continual learning setting in which data from different tasks is presented to the learner in a streaming fashion. A key challenge in this setting is the so-called "catastrophic forgetting problem", in which the performance of the learner in an "old task" decreases when subsequently trained on a "new task". Existing continual learning methods, such as Averaged Gradient Episodic Memory (A-GEM) and Orthogonal Gradient Descent (OGD), address catastrophic forgetting by minimizing the loss for the current task without increasing the loss for previous tasks. However, these methods assume the learner knows when the task changes, which is unrealistic in practice. In this paper, we alleviate the need to provide the algorithm with information about task changes by using an online clustering-based approach on a dynamically updated finite pool of samples or gradients. We thereby successfully counteract catastrophic forgetting in one of the hardest settings, namely: domain-incremental learning, a setting for which the problem was previously unsolved. We showcase the benefits of our approach by applying these ideas to projection-based methods, such as A-GEM and OGD, which lead to task-agnostic versions of them. Experiments on real datasets demonstrate the effectiveness of the proposed strategy and its promising performance compared to state-of-the-art methods.

New roads are being constructed all the time. However, the capabilities of previous deep forecasting models to generalize to new roads not seen in the training data (unseen roads) are rarely explored. In this paper, we introduce a novel setup called a spatio-temporal (ST) split to evaluate the models' capabilities to generalize to unseen roads. In this setup, the models are trained on data from a sample of roads, but tested on roads not seen in the training data. Moreover, we also present a novel framework called Spatial Contrastive Pre-Training (SCPT) where we introduce a spatial encoder module to extract latent features from unseen roads during inference time. This spatial encoder is pre-trained using contrastive learning. During inference, the spatial encoder only requires two days of traffic data on the new roads and does not require any re-training. We also show that the output from the spatial encoder can be used effectively to infer latent node embeddings on unseen roads during inference time. The SCPT framework also incorporates a new layer, named the spatially gated addition (SGA) layer, to effectively combine the latent features from the output of the spatial encoder to existing backbones. Additionally, since there is limited data on the unseen roads, we argue that it is better to decouple traffic signals to trivial-to-capture periodic signals and difficult-to-capture Markovian signals, and for the spatial encoder to only learn the Markovian signals. Finally, we empirically evaluated SCPT using the ST split setup on four real-world datasets. The results showed that adding SCPT to a backbone consistently improves forecasting performance on unseen roads. More importantly, the improvements are greater when forecasting further into the future. The codes are available on GitHub: https://github.com/cruiseresearchgroup/forecasting-on-new-roads .

There is a correlation between adjacent channels of electroencephalogram (EEG), and how to represent this correlation is an issue that is currently being explored. In addition, due to inter-individual differences in EEG signals, this discrepancy results in new subjects need spend a amount of calibration time for EEG-based motor imagery brain-computer interface. In order to solve the above problems, we propose a Dynamic Domain Adaptation Based Deep Learning Network (DADL-Net). First, the EEG data is mapped to the three-dimensional geometric space and its temporal-spatial features are learned through the 3D convolution module, and then the spatial-channel attention mechanism is used to strengthen the features, and the final convolution module can further learn the spatial-temporal information of the features. Finally, to account for inter-subject and cross-sessions differences, we employ a dynamic domain-adaptive strategy, the distance between features is reduced by introducing a Maximum Mean Discrepancy loss function, and the classification layer is fine-tuned by using part of the target domain data. We verify the performance of the proposed method on BCI competition IV 2a and OpenBMI datasets. Under the intra-subject experiment, the accuracy rates of 70.42% and 73.91% were achieved on the OpenBMI and BCIC IV 2a datasets.

A range of applications of multi-modal music information retrieval is centred around the problem of connecting large collections of sheet music (images) to corresponding audio recordings, that is, identifying pairs of audio and score excerpts that refer to the same musical content. One of the typical and most recent approaches to this task employs cross-modal deep learning architectures to learn joint embedding spaces that link the two distinct modalities - audio and sheet music images. While there has been steady improvement on this front over the past years, a number of open problems still prevent large-scale employment of this methodology. In this article we attempt to provide an insightful examination of the current developments on audio-sheet music retrieval via deep learning methods. We first identify a set of main challenges on the road towards robust and large-scale cross-modal music retrieval in real scenarios. We then highlight the steps we have taken so far to address some of these challenges, documenting step-by-step improvement along several dimensions. We conclude by analysing the remaining challenges and present ideas for solving these, in order to pave the way to a unified and robust methodology for cross-modal music retrieval.

Electroencephalogram (EEG) data compression is necessary for wireless recording applications to reduce the amount of data that needs to be transmitted. In this paper, an asymmetrical sparse autoencoder with a discrete cosine transform (DCT) layer is proposed to compress EEG signals. The encoder module of the autoencoder has a combination of a fully connected linear layer and the DCT layer to reduce redundant data using hard-thresholding nonlinearity. Furthermore, the DCT layer includes trainable hard-thresholding parameters and scaling layers to give emphasis or de-emphasis on individual DCT coefficients. Finally, the one-by-one convolutional layer generates the latent space. The sparsity penalty-based cost function is employed to keep the feature map as sparse as possible in the latent space. The latent space data is transmitted to the receiver. The decoder module of the autoencoder is designed using the inverse DCT and two fully connected linear layers to improve the accuracy of data reconstruction. In comparison to other state-of-the-art methods, the proposed method significantly improves the average quality score in various data compression experiments.

The applications of personalized recommender systems are rapidly expanding: encompassing social media, online shopping, search engine results, and more. These systems offer a more efficient way to navigate the vast array of items available. However, alongside this growth, there has been increased recognition of the potential for algorithmic systems to exhibit and perpetuate biases, risking unfairness in personalized domains. In this work, we explicate the inherent tension between personalization and conventional implementations of fairness. As an alternative, we propose equity to achieve fairness in the context of epistemic utility. We provide a mapping between goals and practical implementations and detail policy recommendations across key stakeholders to forge a path towards achieving fairness in personalized systems.

We introduce so-called functional input neural networks defined on a possibly infinite dimensional weighted space with values also in a possibly infinite dimensional output space. To this end, we use an additive family as hidden layer maps and a non-linear activation function applied to each hidden layer. Relying on Stone-Weierstrass theorems on weighted spaces, we can prove a global universal approximation result for generalizations of continuous functions going beyond the usual approximation on compact sets. This then applies in particular to approximation of (non-anticipative) path space functionals via functional input neural networks. As a further application of the weighted Stone-Weierstrass theorem we prove a global universal approximation result for linear functions of the signature. We also introduce the viewpoint of Gaussian process regression in this setting and show that the reproducing kernel Hilbert space of the signature kernels are Cameron-Martin spaces of certain Gaussian processes. This paves the way towards uncertainty quantification for signature kernel regression.

Most existing works on federated bandits take it for granted that all clients are altruistic about sharing their data with the server for the collective good whenever needed. Despite their compelling theoretical guarantee on performance and communication efficiency, this assumption is overly idealistic and oftentimes violated in practice, especially when the algorithm is operated over self-interested clients, who are reluctant to share data without explicit benefits. Negligence of such self-interested behaviors can significantly affect the learning efficiency and even the practical operability of federated bandit learning. In light of this, we aim to spark new insights into this under-explored research area by formally introducing an incentivized communication problem for federated bandits, where the server shall motivate clients to share data by providing incentives. Without loss of generality, we instantiate this bandit problem with the contextual linear setting and propose the first incentivized communication protocol, namely, Inc-FedUCB, that achieves near-optimal regret with provable communication and incentive cost guarantees. Extensive empirical experiments on both synthetic and real-world datasets further validate the effectiveness of the proposed method across various environments.

Federated learning is a distributed machine learning system that uses participants' data to train an improved global model. In federated learning, participants cooperatively train a global model, and they will receive the global model and payments. Rational participants try to maximize their individual utility, and they will not input their high-quality data truthfully unless they are provided with satisfactory payments based on their data quality. Furthermore, federated learning benefits from the cooperative contributions of participants. Accordingly, how to establish an incentive mechanism that both incentivizes inputting data truthfully and promotes stable cooperation has become an important issue to consider. In this paper, we introduce a data sharing game model for federated learning and employ game-theoretic approaches to design a core-selecting incentive mechanism by utilizing a popular concept in cooperative games, the core. In federated learning, the core can be empty, resulting in the core-selecting mechanism becoming infeasible. To address this, our core-selecting mechanism employs a relaxation method and simultaneously minimizes the benefits of inputting false data for all participants. However, this mechanism is computationally expensive because it requires aggregating exponential models for all possible coalitions, which is infeasible in federated learning. To address this, we propose an efficient core-selecting mechanism based on sampling approximation that only aggregates models on sampled coalitions to approximate the exact result. Extensive experiments verify that the efficient core-selecting mechanism can incentivize inputting high-quality data and stable cooperation, while it reduces computational overhead compared to the core-selecting mechanism.

The study of complex networks has significantly advanced our understanding of community structures which serves as a crucial feature of real-world graphs. Detecting communities in graphs is a challenging problem with applications in sociology, biology, and computer science. Despite the efforts of an interdisciplinary community of scientists, a satisfactory solution to this problem has not yet been achieved. This review article delves into the topic of community detection in graphs, which serves as a crucial role in understanding the organization and functioning of complex systems. We begin by introducing the concept of community structure, which refers to the arrangement of vertices into clusters, with strong internal connections and weaker connections between clusters. Then, we provide a thorough exposition of various community detection methods, including a new method designed by us. Additionally, we explore real-world applications of community detection in diverse networks. In conclusion, this comprehensive review provides a deep understanding of community detection in graphs. It serves as a valuable resource for researchers and practitioners in multiple disciplines, offering insights into the challenges, methodologies, and applications of community detection in complex networks.

Large language models (LLMs) are increasingly being used for generating text in a variety of use cases, including journalistic news articles. Given the potential malicious nature in which these LLMs can be used to generate disinformation at scale, it is important to build effective detectors for such AI-generated text. Given the surge in development of new LLMs, acquiring labeled training data for supervised detectors is a bottleneck. However, there might be plenty of unlabeled text data available, without information on which generator it came from. In this work we tackle this data problem, in detecting AI-generated news text, and frame the problem as an unsupervised domain adaptation task. Here the domains are the different text generators, i.e. LLMs, and we assume we have access to only the labeled source data and unlabeled target data. We develop a Contrastive Domain Adaptation framework, called ConDA, that blends standard domain adaptation techniques with the representation power of contrastive learning to learn domain invariant representations that are effective for the final unsupervised detection task. Our experiments demonstrate the effectiveness of our framework, resulting in average performance gains of 31.7% from the best performing baselines, and within 0.8% margin of a fully supervised detector. All our code and data is available at https://github.com/AmritaBh/ConDA-gen-text-detection.

Self-supervised learning (SSL) has gained remarkable success, for which contrastive learning (CL) plays a key role. However, the recent development of new non-CL frameworks has achieved comparable or better performance with high improvement potential, prompting researchers to enhance these frameworks further. Assimilating CL into non-CL frameworks has been thought to be beneficial, but empirical evidence indicates no visible improvements. In view of that, this paper proposes a strategy of performing CL along the dimensional direction instead of along the batch direction as done in conventional contrastive learning, named Dimensional Contrastive Learning (DimCL). DimCL aims to enhance the feature diversity, and it can serve as a regularizer to prior SSL frameworks. DimCL has been found to be effective, and the hardness-aware property is identified as a critical reason for its success. Extensive experimental results reveal that assimilating DimCL into SSL frameworks leads to performance improvement by a non-trivial margin on various datasets and backbone architectures.

We study the problem of in-context learning (ICL) with large language models (LLMs) on private datasets. This scenario poses privacy risks, as LLMs may leak or regurgitate the private examples demonstrated in the prompt. We propose a novel algorithm that generates synthetic few-shot demonstrations from the private dataset with formal differential privacy (DP) guarantees, and show empirically that it can achieve effective ICL. We conduct extensive experiments on standard benchmarks and compare our algorithm with non-private ICL and zero-shot solutions. Our results demonstrate that our algorithm can achieve competitive performance with strong privacy levels. These results open up new possibilities for ICL with privacy protection for a broad range of applications.  ( 2 min )

Quantum machine learning is a promising programming paradigm for the optimization of quantum algorithms in the current era of noisy intermediate scale quantum (NISQ) computers. A fundamental challenge in quantum machine learning is generalization, as the designer targets performance under testing conditions, while having access only to limited training data. Existing generalization analyses, while identifying important general trends and scaling laws, cannot be used to assign reliable and informative "error bars" to the decisions made by quantum models. In this article, we propose a general methodology that can reliably quantify the uncertainty of quantum models, irrespective of the amount of training data, of the number of shots, of the ansatz, of the training algorithm, and of the presence of quantum hardware noise. The approach, which builds on probabilistic conformal prediction, turns an arbitrary, possibly small, number of shots from a pre-trained quantum model into a set prediction, e.g., an interval, that provably contains the true target with any desired coverage level. Experimental results confirm the theoretical calibration guarantees of the proposed framework, referred to as quantum conformal prediction.

Hierarchical reinforcement learning has been a compelling approach for achieving goal directed behavior over long sequences of actions. However, it has been challenging to implement in realistic or open-ended environments. A main challenge has been to find the right space of sub-goals over which to instantiate a hierarchy. We present a novel approach where we use data from humans solving these tasks to softly supervise the goal space for a set of long range tasks in a 3D embodied environment. In particular, we use unconstrained natural language to parameterize this space. This has two advantages: first, it is easy to generate this data from naive human participants; second, it is flexible enough to represent a vast range of sub-goals in human-relevant tasks. Our approach outperforms agents that clone expert behavior on these tasks, as well as HRL from scratch without this supervised sub-goal space. Our work presents a novel approach to combining human expert supervision with the benefits and flexibility of reinforcement learning.

Generalized Additive Models (GAMs) are widely used explainable-by-design models in various applications. GAMs assume that the output can be represented as a sum of univariate functions, referred to as components. However, this assumption fails in ML problems where the output depends on multiple features simultaneously. In these cases, GAMs fail to capture the interaction terms of the underlying function, leading to subpar accuracy. To (partially) address this issue, we propose Regionally Additive Models (RAMs), a novel class of explainable-by-design models. RAMs identify subregions within the feature space where interactions are minimized. Within these regions, it is more accurate to express the output as a sum of univariate functions (components). Consequently, RAMs fit one component per subregion of each feature instead of one component per feature. This approach yields a more expressive model compared to GAMs while retaining interpretability. The RAM framework consists of three steps. Firstly, we train a black-box model. Secondly, using Regional Effect Plots, we identify subregions where the black-box model exhibits near-local additivity. Lastly, we fit a GAM component for each identified subregion. We validate the effectiveness of RAMs through experiments on both synthetic and real-world datasets. The results confirm that RAMs offer improved expressiveness compared to GAMs while maintaining interpretability.

We consider the problem of estimating the learning rate in adaptive methods, such as Adagrad and Adam. We describe two techniques, Prodigy and Resetting, to provably estimate the distance to the solution $D$, which is needed to set the learning rate optimally. Our techniques are modifications of the D-Adaptation method for learning-rate-free learning. Our methods improve upon the convergence rate of D-Adaptation by a factor of $O(\sqrt{\log(D/d_0)})$, where $d_0$ is the initial estimate of $D$. We test our methods on 12 common logistic-regression benchmark datasets, VGG11 and ResNet-50 training on CIFAR10, ViT training on Imagenet, LSTM training on IWSLT14, DLRM training on Criteo dataset, VarNet on Knee MRI dataset, as well as RoBERTa and GPT transformer training on BookWiki. Our experimental results show that our approaches consistently outperform D-Adaptation and reach test accuracy values close to that of hand-tuned Adam.

The use of skills (a.k.a., options) can greatly accelerate exploration in reinforcement learning, especially when only sparse reward signals are available. While option discovery methods have been proposed for individual agents, in multi-agent reinforcement learning settings, discovering collaborative options that can coordinate the behavior of multiple agents and encourage them to visit the under-explored regions of their joint state space has not been considered. In this case, we propose Multi-agent Deep Covering Option Discovery, which constructs the multi-agent options through minimizing the expected cover time of the multiple agents' joint state space. Also, we propose a novel framework to adopt the multi-agent options in the MARL process. In practice, a multi-agent task can usually be divided into some sub-tasks, each of which can be completed by a sub-group of the agents. Therefore, our algorithm framework first leverages an attention mechanism to find collaborative agent sub-groups that would benefit most from coordinated actions. Then, a hierarchical algorithm, namely HA-MSAC, is developed to learn the multi-agent options for each sub-group to complete their sub-tasks first, and then to integrate them through a high-level policy as the solution of the whole task. This hierarchical option construction allows our framework to strike a balance between scalability and effective collaboration among the agents. The evaluation based on multi-agent collaborative tasks shows that the proposed algorithm can effectively capture the agent interactions with the attention mechanism, successfully identify multi-agent options, and significantly outperforms prior works using single-agent options or no options, in terms of both faster exploration and higher task rewards.

A hypothesis class admits a sample compression scheme, if for every sample labeled by a hypothesis from the class, it is possible to retain only a small subsample, using which the labels on the entire sample can be inferred. The size of the compression scheme is an upper bound on the size of the subsample produced. Every learnable binary hypothesis class (which must necessarily have finite VC dimension) admits a sample compression scheme of size only a finite function of its VC dimension, independent of the sample size. For multiclass hypothesis classes, the analog of VC dimension is the DS dimension. We show that the analogous statement pertaining to sample compression is not true for multiclass hypothesis classes: every learnable multiclass hypothesis class, which must necessarily have finite DS dimension, does not admit a sample compression scheme of size only a finite function of its DS dimension.

Designing reward functions is a longstanding challenge in reinforcement learning (RL); it requires specialized knowledge or domain data, leading to high costs for development. To address this, we introduce Text2Reward, a data-free framework that automates the generation of dense reward functions based on large language models (LLMs). Given a goal described in natural language, Text2Reward generates dense reward functions as an executable program grounded in a compact representation of the environment. Unlike inverse RL and recent work that uses LLMs to write sparse reward codes, Text2Reward produces interpretable, free-form dense reward codes that cover a wide range of tasks, utilize existing packages, and allow iterative refinement with human feedback. We evaluate Text2Reward on two robotic manipulation benchmarks (ManiSkill2, MetaWorld) and two locomotion environments of MuJoCo. On 13 of the 17 manipulation tasks, policies trained with generated reward codes achieve similar or better task success rates and convergence speed than expert-written reward codes. For locomotion tasks, our method learns six novel locomotion behaviors with a success rate exceeding 94%. Furthermore, we show that the policies trained in the simulator with our method can be deployed in the real world. Finally, Text2Reward further improves the policies by refining their reward functions with human feedback. Video results are available at https://text-to-reward.github.io

The problem of function approximation by neural dynamical systems has typically been approached in a top-down manner: Any continuous function can be approximated to an arbitrary accuracy by a sufficiently complex model with a given architecture. This can lead to high-complexity controls which are impractical in applications. In this paper, we take the opposite, constructive approach: We impose various structural restrictions on system dynamics and consequently characterize the class of functions that can be realized by such a system. The systems are implemented as a cascade interconnection of a neural stochastic differential equation (Neural SDE), a deterministic dynamical system, and a readout map. Both probabilistic and geometric (Lie-theoretic) methods are used to characterize the classes of functions realized by such systems.

The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of Artificial Intelligence by promoting the joint design of resource-constrained IoT hardware devices and their learning-based software architectures. TinyML carries an essential role within the fourth and fifth industrial revolutions in helping societies, economies, and individuals employ effective AI-infused computing technologies (e.g., smart cities, automotive, and medical robotics). Given its multidisciplinary nature, the field of TinyML has been approached from many different angles: this comprehensive survey wishes to provide an up-to-date overview focused on all the learning algorithms within TinyML-based solutions. The survey is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodological flow, allowing for a systematic and complete literature survey. In particular, firstly we will examine the three different workflows for implementing a TinyML-based system, i.e., ML-oriented, HW-oriented, and co-design. Secondly, we propose a taxonomy that covers the learning panorama under the TinyML lens, examining in detail the different families of model optimization and design, as well as the state-of-the-art learning techniques. Thirdly, this survey will present the distinct features of hardware devices and software tools that represent the current state-of-the-art for TinyML intelligent edge applications. Finally, we discuss the challenges and future directions.

This work is devoted to the study of the probability of immunity, i.e. the effect occurs whether exposed or not. We derive necessary and sufficient conditions for non-immunity and $\epsilon$-bounded immunity, i.e. the probability of immunity is zero and $\epsilon$-bounded, respectively. The former allows us to estimate the probability of benefit (i.e., the effect occurs if and only if exposed) from a randomized controlled trial, and the latter allows us to produce bounds of the probability of benefit that are tighter than the existing ones. We also introduce the concept of indirect immunity (i.e., through a mediator) and repeat our previous analysis for it. Finally, we propose a method for sensitivity analysis of the probability of immunity under unmeasured confounding.

Monte Carlo (MC) approximation has been used as the standard computation approach for the Sliced Wasserstein (SW) distance, which has an intractable expectation in its analytical form. However, the MC method is not optimal in terms of minimizing the absolute approximation error. To provide a better class of empirical SW, we propose quasi-sliced Wasserstein (QSW) approximations that rely on Quasi-Monte Carlo (QMC) methods. For a comprehensive investigation of QMC for SW, we focus on the 3D setting, specifically computing the SW between probability measures in three dimensions. In greater detail, we empirically verify various ways of constructing QMC points sets on the 3D unit-hypersphere, including Gaussian-based mapping, equal area mapping, generalized spiral points, and optimizing discrepancy energies. Furthermore, to obtain an unbiased estimation for stochastic optimization, we extend QSW into Randomized Quasi-Sliced Wasserstein (RQSW) by introducing randomness to the discussed low-discrepancy sequences. For theoretical properties, we prove the asymptotic convergence of QSW and the unbiasedness of RQSW. Finally, we conduct experiments on various 3D tasks, such as point-cloud comparison, point-cloud interpolation, image style transfer, and training deep point-cloud autoencoders, to demonstrate the favorable performance of the proposed QSW and RQSW variants.  ( 2 min )

Quantization of deep neural networks (DNN) has become a key element in the efforts of embedding such networks on end-user devices. However, current quantization methods usually suffer from costly accuracy degradation. In this paper, we propose a new method for Enhanced Post Training Quantization named EPTQ. The method is based on knowledge distillation with an adaptive weighting of layers. In addition, we introduce a new label-free technique for approximating the Hessian trace of the task loss, named Label-Free Hessian. This technique removes the requirement of a labeled dataset for computing the Hessian. The adaptive knowledge distillation uses the Label-Free Hessian technique to give greater attention to the sensitive parts of the model while performing the optimization. Empirically, by employing EPTQ we achieve state-of-the-art results on a wide variety of models, tasks, and datasets, including ImageNet classification, COCO object detection, and Pascal-VOC for semantic segmentation. We demonstrate the performance and compatibility of EPTQ on an extended set of architectures, including CNNs, Transformers, hybrid, and MLP-only models.  ( 2 min )

This paper introduces Bayesian Flow Networks (BFNs), a new class of generative model in which the parameters of a set of independent distributions are modified with Bayesian inference in the light of noisy data samples, then passed as input to a neural network that outputs a second, interdependent distribution. Starting from a simple prior and iteratively updating the two distributions yields a generative procedure similar to the reverse process of diffusion models; however it is conceptually simpler in that no forward process is required. Discrete and continuous-time loss functions are derived for continuous, discretised and discrete data, along with sample generation procedures. Notably, the network inputs for discrete data lie on the probability simplex, and are therefore natively differentiable, paving the way for gradient-based sample guidance and few-step generation in discrete domains such as language modelling. The loss function directly optimises data compression and places no restrictions on the network architecture. In our experiments BFNs achieve competitive log-likelihoods for image modelling on dynamically binarized MNIST and CIFAR-10, and outperform all known discrete diffusion models on the text8 character-level language modelling task.

The recommendation of appropriate development pathways, also known as ecological civilization patterns for achieving Sustainable Development Goals (namely, sustainable development patterns), are of utmost importance for promoting ecological, economic, social, and resource sustainability in a specific region. To achieve this, the recommendation process must carefully consider the region's natural, environmental, resource, and economic characteristics. However, current recommendation algorithms in the field of computer science fall short in adequately addressing the spatial heterogeneity related to environment and sparsity of regional historical interaction data, which limits their effectiveness in recommending sustainable development patterns. To overcome these challenges, this paper proposes a method called User Graph after Pruning and Intent Graph (UGPIG). Firstly, we utilize the high-density linking capability of the pruned User Graph to address the issue of spatial heterogeneity neglect in recommendation algorithms. Secondly, we construct an Intent Graph by incorporating the intent network, which captures the preferences for attributes including environmental elements of target regions. This approach effectively alleviates the problem of sparse historical interaction data in the region. Through extensive experiments, we demonstrate that UGPIG outperforms state-of-the-art recommendation algorithms like KGCN, KGAT, and KGIN in sustainable development pattern recommendations, with a maximum improvement of 9.61% in Top-3 recommendation performance.  ( 2 min )

The prevalence of ubiquitous location-aware devices and mobile Internet enables us to collect massive individual-level trajectory dataset from users. Such trajectory big data bring new opportunities to human mobility research but also raise public concerns with regard to location privacy. In this work, we present the Conditional Adversarial Trajectory Synthesis (CATS), a deep-learning-based GeoAI methodological framework for privacy-preserving trajectory data generation and publication. CATS applies K-anonymity to the underlying spatiotemporal distributions of human movements, which provides a distributional-level strong privacy guarantee. By leveraging conditional adversarial training on K-anonymized human mobility matrices, trajectory global context learning using the attention-based mechanism, and recurrent bipartite graph matching of adjacent trajectory points, CATS is able to reconstruct trajectory topology from conditionally sampled locations and generate high-quality individual-level synthetic trajectory data, which can serve as supplements or alternatives to raw data for privacy-preserving trajectory data publication. The experiment results on over 90k GPS trajectories show that our method has a better performance in privacy preservation, spatiotemporal characteristic preservation, and downstream utility compared with baseline methods, which brings new insights into privacy-preserving human mobility research using generative AI techniques and explores data ethics issues in GIScience.

Motivated by applications in queueing theory, we consider a stochastic control problem whose state space is the $d$-dimensional positive orthant. The controlled process $Z$ evolves as a reflected Brownian motion whose covariance matrix is exogenously specified, as are its directions of reflection from the orthant's boundary surfaces. A system manager chooses a drift vector $\theta(t)$ at each time $t$ based on the history of $Z$, and the cost rate at time $t$ depends on both $Z(t)$ and $\theta(t)$. In our initial problem formulation, the objective is to minimize expected discounted cost over an infinite planning horizon, after which we treat the corresponding ergodic control problem. Extending earlier work by Han et al. (Proceedings of the National Academy of Sciences, 2018, 8505-8510), we develop and illustrate a simulation-based computational method that relies heavily on deep neural network technology. For test problems studied thus far, our method is accurate to within a fraction of one percent, and is computationally feasible in dimensions up to at least $d=30$.

We consider the problem of training private recommendation models with access to public item features. Training with Differential Privacy (DP) offers strong privacy guarantees, at the expense of loss in recommendation quality. We show that incorporating public item features during training can help mitigate this loss in quality. We propose a general approach based on collective matrix factorization (CMF), that works by simultaneously factorizing two matrices: the user feedback matrix (representing sensitive data) and an item feature matrix that encodes publicly available (non-sensitive) item information. The method is conceptually simple, easy to tune, and highly scalable. It can be applied to different types of public item data, including: (1) categorical item features; (2) item-item similarities learned from public sources; and (3) publicly available user feedback. Furthermore, these data modalities can be collectively utilized to fully leverage public data. Evaluating our method on a standard DP recommendation benchmark, we find that using public item features significantly narrows the quality gap between private models and their non-private counterparts. As privacy constraints become more stringent, models rely more heavily on public side features for recommendation. This results in a smooth transition from collaborative filtering to item-based contextual recommendations.

This paper surveys research works in the quickly advancing field of instruction tuning (IT), a crucial technique to enhance the capabilities and controllability of large language models (LLMs). Instruction tuning refers to the process of further training LLMs on a dataset consisting of \textsc{(instruction, output)} pairs in a supervised fashion, which bridges the gap between the next-word prediction objective of LLMs and the users' objective of having LLMs adhere to human instructions. In this work, we make a systematic review of the literature, including the general methodology of IT, the construction of IT datasets, the training of IT models, and applications to different modalities, domains and applications, along with an analysis on aspects that influence the outcome of IT (e.g., generation of instruction outputs, size of the instruction dataset, etc). We also review the potential pitfalls of IT along with criticism against it, along with efforts pointing out current deficiencies of existing strategies and suggest some avenues for fruitful research.

An important task in the field of sensor technology is the efficient implementation of adaptation procedures of measurements from one sensor to another sensor of identical design. One idea is to use the estimation of an affine transformation between different systems, which can be improved by the knowledge of experts. This paper presents an improved solution from Glacier Research that was published back in 1973. It is shown that this solution can be adapted for software calibration of sensors, implementation of expert-based adaptation, and federated learning methods. We evaluate our research with simulations and also with real measured data of a multi-sensor board with 8 identical sensors. The results show an improvement for both the simulation and the experiments with real data.

Music streaming services heavily rely on their recommendation engines to continuously provide content to their consumers. Sequential recommendation consequently has seen considerable attention in current literature, where state of the art approaches focus on self-attentive models leveraging contextual information such as long and short-term user history and item features; however, most of these studies focus on long-form content domains (retail, movie, etc.) rather than short-form, such as music. Additionally, many do not explore incorporating negative session-level feedback during training. In this study, we investigate the use of transformer-based self-attentive architectures to learn implicit session-level information for sequential music recommendation. We additionally propose a contrastive learning task to incorporate negative feedback (e.g skipped tracks) to promote positive hits and penalize negative hits. This task is formulated as a simple loss term that can be incorporated into a variety of deep learning architectures for sequential recommendation. Our experiments show that this results in consistent performance gains over the baseline architectures ignoring negative user feedback.

A sequential pattern with negation, or negative sequential pattern, takes the form of a sequential pattern for which the negation symbol may be used in front of some of the pattern's itemsets. Intuitively, such a pattern occurs in a sequence if negated itemsets are absent in the sequence. Recent work has shown that different semantics can be attributed to these pattern forms, and that state-of-the-art algorithms do not extract the same sets of patterns. This raises the important question of the interpretability of sequential pattern with negation. In this study, our focus is on exploring how potential users perceive negation in sequential patterns. Our aim is to determine whether specific semantics are more "intuitive" than others and whether these align with the semantics employed by one or more state-of-the-art algorithms. To achieve this, we designed a questionnaire to reveal the semantics' intuition of each user. This article presents both the design of the questionnaire and an in-depth analysis of the 124 responses obtained. The outcomes indicate that two of the semantics are predominantly intuitive; however, neither of them aligns with the semantics of the primary state-of-the-art algorithms. As a result, we provide recommendations to account for this disparity in the conclusions drawn.

Initialization of neural network weights plays a pivotal role in determining their performance. Feature Imitating Networks (FINs) offer a novel strategy by initializing weights to approximate specific closed-form statistical features, setting a promising foundation for deep learning architectures. While the applicability of FINs has been chiefly tested in biomedical domains, this study extends its exploration into other time series datasets. Three different experiments are conducted in this study to test the applicability of imitating Tsallis entropy for performance enhancement: Bitcoin price prediction, speech emotion recognition, and chronic neck pain detection. For the Bitcoin price prediction, models embedded with FINs reduced the root mean square error by around 1000 compared to the baseline. In the speech emotion recognition task, the FIN-augmented model increased classification accuracy by over 3 percent. Lastly, in the CNP detection experiment, an improvement of about 7 percent was observed compared to established classifiers. These findings validate the broad utility and potency of FINs in diverse applications.  ( 2 min )

Programmers and researchers are increasingly developing surrogates of programs, models of a subset of the observable behavior of a given program, to solve a variety of software development challenges. Programmers train surrogates from measurements of the behavior of a program on a dataset of input examples. A key challenge of surrogate construction is determining what training data to use to train a surrogate of a given program. We present a methodology for sampling datasets to train neural-network-based surrogates of programs. We first characterize the proportion of data to sample from each region of a program's input space (corresponding to different execution paths of the program) based on the complexity of learning a surrogate of the corresponding execution path. We next provide a program analysis to determine the complexity of different paths in a program. We evaluate these results on a range of real-world programs, demonstrating that complexity-guided sampling results in empirical improvements in accuracy.  ( 2 min )

Multimodal Large Language Models (MLLMs) that integrate text and other modalities (especially vision) have achieved unprecedented performance in various multimodal tasks. However, due to the unsolved adversarial robustness problem of vision models, MLLMs can have more severe safety and security risks by introducing the vision inputs. In this work, we study the adversarial robustness of Google's Bard, a competitive chatbot to ChatGPT that released its multimodal capability recently, to better understand the vulnerabilities of commercial MLLMs. By attacking white-box surrogate vision encoders or MLLMs, the generated adversarial examples can mislead Bard to output wrong image descriptions with a 22% success rate based solely on the transferability. We show that the adversarial examples can also attack other MLLMs, e.g., a 26% attack success rate against Bing Chat and a 86% attack success rate against ERNIE bot. Moreover, we identify two defense mechanisms of Bard, including face detection and toxicity detection of images. We design corresponding attacks to evade these defenses, demonstrating that the current defenses of Bard are also vulnerable. We hope this work can deepen our understanding on the robustness of MLLMs and facilitate future research on defenses. Our code is available at https://github.com/thu-ml/Attack-Bard.  ( 2 min )

Self-supervised representation learning has seen remarkable progress in the last few years, with some of the recent methods being able to learn useful image representations without labels. These methods are trained using backpropagation, the de facto standard. Recently, Geoffrey Hinton proposed the forward-forward algorithm as an alternative training method. It utilizes two forward passes and a separate loss function for each layer to train the network without backpropagation. In this study, for the first time, we study the performance of forward-forward vs. backpropagation for self-supervised representation learning and provide insights into the learned representation spaces. Our benchmark employs four standard datasets, namely MNIST, F-MNIST, SVHN and CIFAR-10, and three commonly used self-supervised representation learning techniques, namely rotation, flip and jigsaw. Our main finding is that while the forward-forward algorithm performs comparably to backpropagation during (self-)supervised training, the transfer performance is significantly lagging behind in all the studied settings. This may be caused by a combination of factors, including having a loss function for each layer and the way the supervised training is realized in the forward-forward paradigm. In comparison to backpropagation, the forward-forward algorithm focuses more on the boundaries and drops part of the information unnecessary for making decisions which harms the representation learning goal. Further investigation and research are necessary to stabilize the forward-forward strategy for self-supervised learning, to work beyond the datasets and configurations demonstrated by Geoffrey Hinton.  ( 2 min )

The decision-making process in real-world implementations has been affected by a growing reliance on data-driven models. We investigated the synergetic pattern between the data-driven methods, empirical domain knowledge, and first-principles simulations. We showed the potential risk of biased results when using data-driven models without causal analysis. Using a case study assessing the implication of several design solutions on the energy consumption of a building, we proved the necessity of causal analysis during the data-driven modeling process. We concluded that: (a) Data-driven models' accuracy assessment or domain knowledge screening may not rule out biased and spurious results; (b) Data-driven models' feature selection should involve careful consideration of causal relationships, especially colliders; (c) Causal analysis results can be used as an aid to first-principles simulation design and parameter checking to avoid cognitive biases. We proved the benefits of causal analysis when applied to data-driven models in building engineering.  ( 2 min )

Many applications of cross-modal music retrieval are related to connecting sheet music images to audio recordings. A typical and recent approach to this is to learn, via deep neural networks, a joint embedding space that correlates short fixed-size snippets of audio and sheet music by means of an appropriate similarity structure. However, two challenges that arise out of this strategy are the requirement of strongly aligned data to train the networks, and the inherent discrepancies of musical content between audio and sheet music snippets caused by local and global tempo differences. In this paper, we address these two shortcomings by designing a cross-modal recurrent network that learns joint embeddings that can summarize longer passages of corresponding audio and sheet music. The benefits of our method are that it only requires weakly aligned audio-sheet music pairs, as well as that the recurrent network handles the non-linearities caused by tempo variations between audio and sheet music. We conduct a number of experiments on synthetic and real piano data and scores, showing that our proposed recurrent method leads to more accurate retrieval in all possible configurations.  ( 2 min )

This work describes the TrueLearn Python library, which contains a family of online learning Bayesian models for building educational (or more generally, informational) recommendation systems. This family of models was designed following the "open learner" concept, using humanly-intuitive user representations. For the sake of interpretability and putting the user in control, the TrueLearn library also contains different representations to help end-users visualise the learner models, which may in the future facilitate user interaction with their own models. Together with the library, we include a previously publicly released implicit feedback educational dataset with evaluation metrics to measure the performance of the models. The extensive documentation and coding examples make the library highly accessible to both machine learning developers and educational data mining and learning analytic practitioners. The library and the support documentation with examples are available at https://truelearn.readthedocs.io/en/latest.  ( 2 min )

Efficient training of large-scale graph neural networks (GNNs) has been studied with a specific focus on reducing their memory consumption. Work by Liu et al. (2022) proposed extreme activation compression (EXACT) which demonstrated drastic reduction in memory consumption by performing quantization of the intermediate activation maps down to using INT2 precision. They showed little to no reduction in performance while achieving large reductions in GPU memory consumption. In this work, we present an improvement to the EXACT strategy by using block-wise quantization of the intermediate activation maps. We experimentally analyze different block sizes and show further reduction in memory consumption (>15%), and runtime speedup per epoch (about 5%) even when performing extreme extents of quantization with similar performance trade-offs as with the original EXACT. Further, we present a correction to the assumptions on the distribution of intermediate activation maps in EXACT (assumed to be uniform) and show improved variance estimations of the quantization and dequantization steps.  ( 2 min )

Cameras are rapidly becoming the choice for on-board sensors towards space rendezvous due to their small form factor and inexpensive power, mass, and volume costs. When it comes to docking, however, they typically serve a secondary role, whereas the main work is done by active sensors such as lidar. This paper documents the development of a proposed AI-based (artificial intelligence) navigation algorithm intending to mature the use of on-board visible wavelength cameras as a main sensor for docking and on-orbit servicing (OOS), reducing the dependency on lidar and greatly reducing costs. Specifically, the use of AI enables the expansion of the relative navigation solution towards multiple classes of scenarios, e.g., in terms of targets or illumination conditions, which would otherwise have to be crafted on a case-by-case manner using classical image processing methods. Multiple convolutional neural network (CNN) backbone architectures are benchmarked on synthetically generated data of docking manoeuvres with the International Space Station (ISS), achieving position and attitude estimates close to 1% range-normalised and 1 deg, respectively. The integration of the solution with a physical prototype of the refuelling mechanism is validated in laboratory using a robotic arm to simulate a berthing procedure.  ( 2 min )

In this paper, we study the effect of popularity degradation bias in the context of local music recommendations. Specifically, we examine how accurate two top-performing recommendation algorithms, Weight Relevance Matrix Factorization (WRMF) and Multinomial Variational Autoencoder (Mult-VAE), are at recommending artists as a function of artist popularity. We find that both algorithms improve recommendation performance for more popular artists and, as such, exhibit popularity degradation bias. While both algorithms produce a similar level of performance for more popular artists, Mult-VAE shows better relative performance for less popular artists. This suggests that this algorithm should be preferred for local (long-tail) music artist recommendation.  ( 2 min )

Social science often relies on surveys of households and individuals. Dozens of such surveys are regularly administered by the U.S. government. However, they field independent, unconnected samples with specialized questions, limiting research questions to those that can be answered by a single survey. The fusionACS project seeks to integrate data from multiple U.S. household surveys by statistically "fusing" variables from "donor" surveys onto American Community Survey (ACS) microdata. This results in an integrated microdataset of household attributes and well-being dimensions that can be analyzed to address research questions in ways that are not currently possible. The presented data comprise the fusion onto the ACS of select donor variables from the Residential Energy Consumption Survey (RECS) of 2015, the National Household Transportation Survey (NHTS) of 2017, the American Housing Survey (AHS) of 2019, and the Consumer Expenditure Survey - Interview (CEI) for the years 2015-2019. The underlying statistical techniques are included in an open-source $R$ package, fusionModel, that provides generic tools for the creation, analysis, and validation of fused microdata.  ( 2 min )

While training fair machine learning models has been studied extensively in recent years, most developed methods rely on the assumption that the training and test data have similar distributions. In the presence of distribution shifts, fair models may behave unfairly on test data. There have been some developments for fair learning robust to distribution shifts to address this shortcoming. However, most proposed solutions are based on the assumption of having access to the causal graph describing the interaction of different features. Moreover, existing algorithms require full access to data and cannot be used when small batches are used (stochastic/batch implementation). This paper proposes the first stochastic distributionally robust fairness framework with convergence guarantees that do not require knowledge of the causal graph. More specifically, we formulate the fair inference in the presence of the distribution shift as a distributionally robust optimization problem under $L_p$ norm uncertainty sets with respect to the Exponential Renyi Mutual Information (ERMI) as the measure of fairness violation. We then discuss how the proposed method can be implemented in a stochastic fashion. We have evaluated the presented framework's performance and efficiency through extensive experiments on real datasets consisting of distribution shifts.  ( 2 min )

Ad-load balancing is a critical challenge in online advertising systems, particularly in the context of social media platforms, where the goal is to maximize user engagement and revenue while maintaining a satisfactory user experience. This requires the optimization of conflicting objectives, such as user satisfaction and ads revenue. Traditional approaches to ad-load balancing rely on static allocation policies, which fail to adapt to changing user preferences and contextual factors. In this paper, we present an approach that leverages off-policy learning and evaluation from logged bandit feedback. We start by presenting a motivating analysis of the ad-load balancing problem, highlighting the conflicting objectives between user satisfaction and ads revenue. We emphasize the nuances that arise due to user heterogeneity and the dependence on the user's position within a session. Based on this analysis, we define the problem as determining the optimal ad-load for a particular feed fetch. To tackle this problem, we propose an off-policy learning framework that leverages unbiased estimators such as Inverse Propensity Scoring (IPS) and Doubly Robust (DR) to learn and estimate the policy values using offline collected stochastic data. We present insights from online A/B experiments deployed at scale across over 80 million users generating over 200 million sessions, where we find statistically significant improvements in both user satisfaction metrics and ads revenue for the platform.  ( 2 min )

With increasing frequency of high-profile privacy breaches in various online platforms, users are becoming more concerned about their privacy. And recommender system is the core component of online platforms for providing personalized service, consequently, its privacy preservation has attracted great attention. As the gold standard of privacy protection, differential privacy has been widely adopted to preserve privacy in recommender systems. However, existing differentially private recommender systems only consider static and independent interactions, so they cannot apply to sequential recommendation where behaviors are dynamic and dependent. Meanwhile, little attention has been paid on the privacy risk of sensitive user features, most of them only protect user feedbacks. In this work, we propose a novel DIfferentially Private Sequential recommendation framework with a noisy Graph Neural Network approach (denoted as DIPSGNN) to address these limitations. To the best of our knowledge, we are the first to achieve differential privacy in sequential recommendation with dependent interactions. Specifically, in DIPSGNN, we first leverage piecewise mechanism to protect sensitive user features. Then, we innovatively add calibrated noise into aggregation step of graph neural network based on aggregation perturbation mechanism. And this noisy graph neural network can protect sequentially dependent interactions and capture user preferences simultaneously. Extensive experiments demonstrate the superiority of our method over state-of-the-art differentially private recommender systems in terms of better balance between privacy and accuracy.  ( 2 min )

Recent advances in generative modeling, namely Diffusion models, have revolutionized generative modeling, enabling high-quality image generation tailored to user needs. This paper proposes a framework for the generative design of structural components. Specifically, we employ a Latent Diffusion model to generate potential designs of a component that can satisfy a set of problem-specific loading conditions. One of the distinct advantages our approach offers over other generative approaches, such as generative adversarial networks (GANs), is that it permits the editing of existing designs. We train our model using a dataset of geometries obtained from structural topology optimization utilizing the SIMP algorithm. Consequently, our framework generates inherently near-optimal designs. Our work presents quantitative results that support the structural performance of the generated designs and the variability in potential candidate designs. Furthermore, we provide evidence of the scalability of our framework by operating over voxel domains with resolutions varying from $32^3$ to $128^3$. Our framework can be used as a starting point for generating novel near-optimal designs similar to topology-optimized designs.  ( 2 min )

Efficient training of large-scale graph neural networks (GNNs) has been studied with a specific focus on reducing their memory consumption. Work by Liu et al. (2022) proposed extreme activation compression (EXACT) which demonstrated drastic reduction in memory consumption by performing quantization of the intermediate activation maps down to using INT2 precision. They showed little to no reduction in performance while achieving large reductions in GPU memory consumption. In this work, we present an improvement to the EXACT strategy by using block-wise quantization of the intermediate activation maps. We experimentally analyze different block sizes and show further reduction in memory consumption (>15%), and runtime speedup per epoch (about 5%) even when performing extreme extents of quantization with similar performance trade-offs as with the original EXACT. Further, we present a correction to the assumptions on the distribution of intermediate activation maps in EXACT (assumed to be uniform) and show improved variance estimations of the quantization and dequantization steps.  ( 2 min )

This work is devoted to the study of the probability of immunity, i.e. the effect occurs whether exposed or not. We derive necessary and sufficient conditions for non-immunity and $\epsilon$-bounded immunity, i.e. the probability of immunity is zero and $\epsilon$-bounded, respectively. The former allows us to estimate the probability of benefit (i.e., the effect occurs if and only if exposed) from a randomized controlled trial, and the latter allows us to produce bounds of the probability of benefit that are tighter than the existing ones. We also introduce the concept of indirect immunity (i.e., through a mediator) and repeat our previous analysis for it. Finally, we propose a method for sensitivity analysis of the probability of immunity under unmeasured confounding.

Recently, studies on machine learning have focused on methods that use symmetry implicit in a specific manifold as an inductive bias. Grassmann manifolds provide the ability to handle fundamental shapes represented as shape spaces, enabling stable shape analysis. In this paper, we present a novel approach in which we establish the theoretical foundations for learning distributions on the Grassmann manifold via continuous normalization flows, with the explicit goal of generating stable shapes. Our approach facilitates more robust generation by effectively eliminating the influence of extraneous transformations, such as rotations and inversions, through learning and generating within a Grassmann manifolds designed to accommodate the essential shape information of the object. The experimental results indicated that the proposed method can generate high-quality samples by capturing the data structure. Furthermore, the proposed method significantly outperformed state-of-the-art methods in terms of the log-likelihood or evidence lower bound. The results obtained are expected to stimulate further research in this field, leading to advances for stable shape generation and analysis.

Sequential change detection is a classical problem with a variety of applications. However, the majority of prior work has been parametric, for example, focusing on exponential families. We develop a fundamentally new and general framework for sequential change detection when the pre- and post-change distributions are nonparametrically specified (and thus composite). Our procedures come with clean, nonasymptotic bounds on the average run length (frequency of false alarms). In certain nonparametric cases (like sub-Gaussian or sub-exponential), we also provide near-optimal bounds on the detection delay following a changepoint. The primary technical tool that we introduce is called an \emph{e-detector}, which is composed of sums of e-processes -- a fundamental generalization of nonnegative supermartingales -- that are started at consecutive times. We first introduce simple Shiryaev-Roberts and CUSUM-style e-detectors, and then show how to design their mixtures in order to achieve both statistical and computational efficiency. Our e-detector framework can be instantiated to recover classical likelihood-based procedures for parametric problems, as well as yielding the first change detection method for many nonparametric problems. As a running example, we tackle the problem of detecting changes in the mean of a bounded random variable without i.i.d. assumptions, with an application to tracking the performance of a basketball team over multiple seasons.

Thanks to their dependency structure, non-parametric Hidden Markov Models (HMMs) are able to handle model-based clustering without specifying group distributions. The aim of this work is to study the Bayes risk of clustering when using HMMs and to propose associated clustering procedures. We first give a result linking the Bayes risk of classification and the Bayes risk of clustering, which we use to identify the key quantity determining the difficulty of the clustering task. We also give a proof of this result in the i.i.d. framework, which might be of independent interest. Then we study the excess risk of the plugin classifier. All these results are shown to remain valid in the online setting where observations are clustered sequentially. Simulations illustrate our findings.

A hypothesis class admits a sample compression scheme, if for every sample labeled by a hypothesis from the class, it is possible to retain only a small subsample, using which the labels on the entire sample can be inferred. The size of the compression scheme is an upper bound on the size of the subsample produced. Every learnable binary hypothesis class (which must necessarily have finite VC dimension) admits a sample compression scheme of size only a finite function of its VC dimension, independent of the sample size. For multiclass hypothesis classes, the analog of VC dimension is the DS dimension. We show that the analogous statement pertaining to sample compression is not true for multiclass hypothesis classes: every learnable multiclass hypothesis class, which must necessarily have finite DS dimension, does not admit a sample compression scheme of size only a finite function of its DS dimension.  ( 2 min )

Recent years have seen significant activity on the problem of using data for the purpose of learning properties of quantum systems or of processing classical or quantum data via quantum computing. As in classical learning, quantum learning problems involve settings in which the mechanism generating the data is unknown, and the main goal of a learning algorithm is to ensure satisfactory accuracy levels when only given access to data and, possibly, side information such as expert knowledge. This article reviews the complexity of quantum learning using information-theoretic techniques by focusing on data complexity, copy complexity, and model complexity. Copy complexity arises from the destructive nature of quantum measurements, which irreversibly alter the state to be processed, limiting the information that can be extracted about quantum data. For example, in a quantum system, unlike in classical machine learning, it is generally not possible to evaluate the training loss simultaneously on multiple hypotheses using the same quantum data. To make the paper self-contained and approachable by different research communities, we provide extensive background material on classical results from statistical learning theory, as well as on the distinguishability of quantum states. Throughout, we highlight the differences between quantum and classical learning by addressing both supervised and unsupervised learning, and we provide extensive pointers to the literature.

Consider an online convex optimization problem where the loss functions are self-concordant barriers, smooth relative to a convex function $h$, and possibly non-Lipschitz. We analyze the regret of online mirror descent with $h$. Then, based on the result, we prove the following in a unified manner. Denote by $T$ the time horizon and $d$ the parameter dimension. 1. For online portfolio selection, the regret of $\widetilde{\text{EG}}$, a variant of exponentiated gradient due to Helmbold et al., is $\tilde{O} ( T^{2/3} d^{1/3} )$ when $T > 4 d / \log d$. This improves on the original $\tilde{O} ( T^{3/4} d^{1/2} )$ regret bound for $\widetilde{\text{EG}}$. 2. For online portfolio selection, the regret of online mirror descent with the logarithmic barrier is $\tilde{O}(\sqrt{T d})$. The regret bound is the same as that of Soft-Bayes due to Orseau et al. up to logarithmic terms. 3. For online learning quantum states with the logarithmic loss, the regret of online mirror descent with the log-determinant function is also $\tilde{O} ( \sqrt{T d} )$. Its per-iteration time is shorter than all existing algorithms we know.

Simple regret minimization is a critical problem in learning optimal treatment assignment policies across various domains, including healthcare and e-commerce. However, it remains understudied in the contextual bandit setting. We propose a new family of computationally efficient bandit algorithms for the stochastic contextual bandit settings, with the flexibility to be adapted for cumulative regret minimization (with near-optimal minimax guarantees) and simple regret minimization (with SOTA guarantees). Furthermore, our algorithms adapt to model misspecification and extend to the continuous arm settings. These advantages come from constructing and relying on "conformal arm sets" (CASs), which provide a set of arms at every context that encompass the context-specific optimal arm with some probability across the context distribution. Our positive results on simple and cumulative regret guarantees are contrasted by a negative result, which shows that an algorithm can't achieve instance-dependent simple regret guarantees while simultaneously achieving minimax optimal cumulative regret guarantees.  ( 2 min )

The proliferation of fake news has become a significant concern in recent times due to its potential to spread misinformation and manipulate public opinion. This paper presents a comprehensive study on detecting fake news in Brazilian Portuguese, focusing on journalistic-type news. We propose a machine learning-based approach that leverages natural language processing techniques, including TF-IDF and Word2Vec, to extract features from textual data. We evaluate the performance of various classification algorithms, such as logistic regression, support vector machine, random forest, AdaBoost, and LightGBM, on a dataset containing both true and fake news articles. The proposed approach achieves high accuracy and F1-Score, demonstrating its effectiveness in identifying fake news. Additionally, we developed a user-friendly web platform, fakenewsbr.com, to facilitate the verification of news articles' veracity. Our platform provides real-time analysis, allowing users to assess the likelihood of fake news articles. Through empirical analysis and comparative studies, we demonstrate the potential of our approach to contribute to the fight against the spread of fake news and promote more informed media consumption.

Structure learning is the crux of causal inference. Notably, causal discovery (CD) algorithms are brittle when data is scarce, possibly inferring imprecise causal relations that contradict expert knowledge -- especially when considering latent confounders. To aggravate the issue, most CD methods do not provide uncertainty estimates, making it hard for users to interpret results and improve the inference process. Surprisingly, while CD is a human-centered affair, no works have focused on building methods that both 1) output uncertainty estimates that can be verified by experts and 2) interact with those experts to iteratively refine CD. To solve these issues, we start by proposing to sample (causal) ancestral graphs proportionally to a belief distribution based on a score function, such as the Bayesian information criterion (BIC), using generative flow networks. Then, we leverage the diversity in candidate graphs and introduce an optimal experimental design to iteratively probe the expert about the relations among variables, effectively reducing the uncertainty of our belief over ancestral graphs. Finally, we update our samples to incorporate human feedback via importance sampling. Importantly, our method does not require causal sufficiency (i.e., unobserved confounders may exist). Experiments with synthetic observational data show that our method can accurately sample from distributions over ancestral graphs and that we can greatly improve inference quality with human aid.  ( 2 min )

This work describes the TrueLearn Python library, which contains a family of online learning Bayesian models for building educational (or more generally, informational) recommendation systems. This family of models was designed following the "open learner" concept, using humanly-intuitive user representations. For the sake of interpretability and putting the user in control, the TrueLearn library also contains different representations to help end-users visualise the learner models, which may in the future facilitate user interaction with their own models. Together with the library, we include a previously publicly released implicit feedback educational dataset with evaluation metrics to measure the performance of the models. The extensive documentation and coding examples make the library highly accessible to both machine learning developers and educational data mining and learning analytic practitioners. The library and the support documentation with examples are available at https://truelearn.readthedocs.io/en/latest.

Deep learning's immense capabilities are often constrained by the complexity of its models, leading to an increasing demand for effective sparsification techniques. Bayesian sparsification for deep learning emerges as a crucial approach, facilitating the design of models that are both computationally efficient and competitive in terms of performance across various deep learning applications. The state-of-the-art -- in Bayesian sparsification of deep neural networks -- combines structural shrinkage priors on model weights with an approximate inference scheme based on black-box stochastic variational inference. However, model inversion of the full generative model is exceptionally computationally demanding, especially when compared to standard deep learning of point estimates. In this context, we advocate for the use of Bayesian model reduction (BMR) as a more efficient alternative for pruning of model weights. As a generalization of the Savage-Dickey ratio, BMR allows a post-hoc elimination of redundant model weights based on the posterior estimates under a straightforward (non-hierarchical) generative model. Our comparative study highlights the computational efficiency and the pruning rate of the BMR method relative to the established stochastic variational inference (SVI) scheme, when applied to the full hierarchical generative model. We illustrate the potential of BMR to prune model parameters across various deep learning architectures, from classical networks like LeNet to modern frameworks such as Vision Transformers and MLP-Mixers.

While training fair machine learning models has been studied extensively in recent years, most developed methods rely on the assumption that the training and test data have similar distributions. In the presence of distribution shifts, fair models may behave unfairly on test data. There have been some developments for fair learning robust to distribution shifts to address this shortcoming. However, most proposed solutions are based on the assumption of having access to the causal graph describing the interaction of different features. Moreover, existing algorithms require full access to data and cannot be used when small batches are used (stochastic/batch implementation). This paper proposes the first stochastic distributionally robust fairness framework with convergence guarantees that do not require knowledge of the causal graph. More specifically, we formulate the fair inference in the presence of the distribution shift as a distributionally robust optimization problem under $L_p$ norm uncertainty sets with respect to the Exponential Renyi Mutual Information (ERMI) as the measure of fairness violation. We then discuss how the proposed method can be implemented in a stochastic fashion. We have evaluated the presented framework's performance and efficiency through extensive experiments on real datasets consisting of distribution shifts.

We consider the problem of estimating the learning rate in adaptive methods, such as Adagrad and Adam. We describe two techniques, Prodigy and Resetting, to provably estimate the distance to the solution $D$, which is needed to set the learning rate optimally. Our techniques are modifications of the D-Adaptation method for learning-rate-free learning. Our methods improve upon the convergence rate of D-Adaptation by a factor of $O(\sqrt{\log(D/d_0)})$, where $d_0$ is the initial estimate of $D$. We test our methods on 12 common logistic-regression benchmark datasets, VGG11 and ResNet-50 training on CIFAR10, ViT training on Imagenet, LSTM training on IWSLT14, DLRM training on Criteo dataset, VarNet on Knee MRI dataset, as well as RoBERTa and GPT transformer training on BookWiki. Our experimental results show that our approaches consistently outperform D-Adaptation and reach test accuracy values close to that of hand-tuned Adam.  ( 2 min )

Monte Carlo (MC) approximation has been used as the standard computation approach for the Sliced Wasserstein (SW) distance, which has an intractable expectation in its analytical form. However, the MC method is not optimal in terms of minimizing the absolute approximation error. To provide a better class of empirical SW, we propose quasi-sliced Wasserstein (QSW) approximations that rely on Quasi-Monte Carlo (QMC) methods. For a comprehensive investigation of QMC for SW, we focus on the 3D setting, specifically computing the SW between probability measures in three dimensions. In greater detail, we empirically verify various ways of constructing QMC points sets on the 3D unit-hypersphere, including Gaussian-based mapping, equal area mapping, generalized spiral points, and optimizing discrepancy energies. Furthermore, to obtain an unbiased estimation for stochastic optimization, we extend QSW into Randomized Quasi-Sliced Wasserstein (RQSW) by introducing randomness to the discussed low-discrepancy sequences. For theoretical properties, we prove the asymptotic convergence of QSW and the unbiasedness of RQSW. Finally, we conduct experiments on various 3D tasks, such as point-cloud comparison, point-cloud interpolation, image style transfer, and training deep point-cloud autoencoders, to demonstrate the favorable performance of the proposed QSW and RQSW variants.  ( 2 min )

We demonstrate the first algorithms for the problem of regression for generalized linear models (GLMs) in the presence of additive oblivious noise. We assume we have sample access to examples $(x, y)$ where $y$ is a noisy measurement of $g(w^* \cdot x)$. In particular, \new{the noisy labels are of the form} $y = g(w^* \cdot x) + \xi + \epsilon$, where $\xi$ is the oblivious noise drawn independently of $x$ \new{and satisfies} $\Pr[\xi = 0] \geq o(1)$, and $\epsilon \sim \mathcal N(0, \sigma^2)$. Our goal is to accurately recover a \new{parameter vector $w$ such that the} function $g(w \cdot x)$ \new{has} arbitrarily small error when compared to the true values $g(w^* \cdot x)$, rather than the noisy measurements $y$. We present an algorithm that tackles \new{this} problem in its most general distribution-independent setting, where the solution may not \new{even} be identifiable. \new{Our} algorithm returns \new{an accurate estimate of} the solution if it is identifiable, and otherwise returns a small list of candidates, one of which is close to the true solution. Furthermore, we \new{provide} a necessary and sufficient condition for identifiability, which holds in broad settings. \new{Specifically,} the problem is identifiable when the quantile at which $\xi + \epsilon = 0$ is known, or when the family of hypotheses does not contain candidates that are nearly equal to a translated $g(w^* \cdot x) + A$ for some real number $A$, while also having large error when compared to $g(w^* \cdot x)$. This is the first \new{algorithmic} result for GLM regression \new{with oblivious noise} which can handle more than half the samples being arbitrarily corrupted. Prior work focused largely on the setting of linear regression, and gave algorithms under restrictive assumptions.  ( 3 min )

Modern deep networks are trained with stochastic gradient descent (SGD) whose key parameters are the number of data considered at each step or batch size $B$, and the step size or learning rate $\eta$. For small $B$ and large $\eta$, SGD corresponds to a stochastic evolution of the parameters, whose noise amplitude is governed by the `temperature' $T\equiv \eta/B$. Yet this description is observed to break down for sufficiently large batches $B\geq B^*$, or simplifies to gradient descent (GD) when the temperature is sufficiently small. Understanding where these cross-overs take place remains a central challenge. Here we resolve these questions for a teacher-student perceptron classification model, and show empirically that our key predictions still apply to deep networks. Specifically, we obtain a phase diagram in the $B$-$\eta$ plane that separates three dynamical phases: $\textit{(i)}$ a noise-dominated SGD governed by temperature, $\textit{(ii)}$ a large-first-step-dominated SGD and $\textit{(iii)}$ GD. These different phases also corresponds to different regimes of generalization error. Remarkably, our analysis reveals that the batch size $B^*$ separating regimes $\textit{(i)}$ and $\textit{(ii)}$ scale with the size $P$ of the training set, with an exponent that characterizes the hardness of the classification problem.  ( 2 min )

Generalized Additive Models (GAMs) are widely used explainable-by-design models in various applications. GAMs assume that the output can be represented as a sum of univariate functions, referred to as components. However, this assumption fails in ML problems where the output depends on multiple features simultaneously. In these cases, GAMs fail to capture the interaction terms of the underlying function, leading to subpar accuracy. To (partially) address this issue, we propose Regionally Additive Models (RAMs), a novel class of explainable-by-design models. RAMs identify subregions within the feature space where interactions are minimized. Within these regions, it is more accurate to express the output as a sum of univariate functions (components). Consequently, RAMs fit one component per subregion of each feature instead of one component per feature. This approach yields a more expressive model compared to GAMs while retaining interpretability. The RAM framework consists of three steps. Firstly, we train a black-box model. Secondly, using Regional Effect Plots, we identify subregions where the black-box model exhibits near-local additivity. Lastly, we fit a GAM component for each identified subregion. We validate the effectiveness of RAMs through experiments on both synthetic and real-world datasets. The results confirm that RAMs offer improved expressiveness compared to GAMs while maintaining interpretability.  ( 2 min )

We introduce so-called functional input neural networks defined on a possibly infinite dimensional weighted space with values also in a possibly infinite dimensional output space. To this end, we use an additive family as hidden layer maps and a non-linear activation function applied to each hidden layer. Relying on Stone-Weierstrass theorems on weighted spaces, we can prove a global universal approximation result for generalizations of continuous functions going beyond the usual approximation on compact sets. This then applies in particular to approximation of (non-anticipative) path space functionals via functional input neural networks. As a further application of the weighted Stone-Weierstrass theorem we prove a global universal approximation result for linear functions of the signature. We also introduce the viewpoint of Gaussian process regression in this setting and show that the reproducing kernel Hilbert space of the signature kernels are Cameron-Martin spaces of certain Gaussian processes. This paves the way towards uncertainty quantification for signature kernel regression.  ( 2 min )

Initialization of neural network weights plays a pivotal role in determining their performance. Feature Imitating Networks (FINs) offer a novel strategy by initializing weights to approximate specific closed-form statistical features, setting a promising foundation for deep learning architectures. While the applicability of FINs has been chiefly tested in biomedical domains, this study extends its exploration into other time series datasets. Three different experiments are conducted in this study to test the applicability of imitating Tsallis entropy for performance enhancement: Bitcoin price prediction, speech emotion recognition, and chronic neck pain detection. For the Bitcoin price prediction, models embedded with FINs reduced the root mean square error by around 1000 compared to the baseline. In the speech emotion recognition task, the FIN-augmented model increased classification accuracy by over 3 percent. Lastly, in the CNP detection experiment, an improvement of about 7 percent was observed compared to established classifiers. These findings validate the broad utility and potency of FINs in diverse applications.  ( 2 min )

submitted by /u/glenniszen [link] [comments]  ( 8 min )

So I created a voice profile based on recordings of my own voice over on voice.ai. The audio was crisp, loud and clear. I only added about 16 minutes if it though. (A minimum of 15 minutes is recommended) When I take a prerecorded voice clip and feed it through the voice profile I created, it’s just silent for all 15 seconds that it grants you before you upgrade. Then it has the little 2 second audio watermark at the end, where it says “voice AI,” and that I can hear perfectly fine! I’ve searched all over for others who might have had this problem, but it seems like the main problem people have reported difficulties with audio are/were tend to center around the live mode failing to function properly. Has anyone had this issue before? Should I add more audio to the voice profile to make it more well rounded perhaps? If so, how much audio do you typically need to create a solid custom voice profile? Should I just update to the paid version? I don’t really think that would fix it, and I wanted to wait to upgrade until I had some proof that it worked, yanno? I’m not sure what to do… Any ideas? submitted by /u/WhenTheFoxGRINS [link] [comments]  ( 9 min )

submitted by /u/Senior_tasteey [link] [comments]  ( 8 min )

Founders and venture capitalists who flocked to artificial-intelligence startups are learning that turning the chatbot buzz into successful businesses is harder than it seems. Source https://www.wsj.com/tech/ai/ai-startup-buzz-is-facing-a-reality-check-e34babfe submitted by /u/NuseAI [link] [comments]  ( 8 min )

News provided by aibrews.com Genmo releases a new text-to-video model: Genmo Replay v0.1, which generate high-quality videos from text without the need for advanced prompt engineering. Genmo is available for free to create AI videos [Details | Genmo Replay] . OpenAI unveils DALL·E 3 - a major update to the text-to-image model, which will be integrated in ChatGPT. It will be available to ChatGPT Plus and Enterprise users in October, via the API and in Labs later this fall. Creators can now also opt their images out from future training [Details]. Toyota Research Institute has developed a technique, powered by generative AI, that enables teaching robots new manipulation abilities in a single afternoon. Using the same robot, same code, and same setup, TRI taught over 60 different dextero…  ( 11 min )

As AI models get bigger, training them requires more and more computing power. Researchers are looking for ways to train these large AI models without needing Google-scale resources. A new paper proposes LongLoRA, a fine-tuning approach that can extend LLaMA2 7B to 100k context length and 70B model to 32k context length on a single 8× A100 machine. Here are my highlights from the paper: Big one of course: LongLoRA efficiently fine-tunes large AI models on longer texts Key points: Approximates standard attention via "shift short attention" during training Tuning only a subset of weights (LoRA) plus some embeddings & norms Fine-tuned 7B parameter model on 100k tokens with 1 machine Way lower training cost than full fine-tuning for large contexts Close to full fine-tuning performance The core insight is that an approximation of full attention enables efficient training while retaining standard attention for final inference. Combined with selective weight tuning, this really reduces compute needs. I think this demonstrates the potential to train more capable AI without unreasonable resources. Efficient training techniques = more powerful LLMs for the same resources. Full summary here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

It's currently on the 4th place after marketing, consulting, and accounting. And is mostly used in software testing field to: achieve more accurate results have larger test coverage receive low learning curve get faster QA completion I was actually quite surprised when I read, I thought that information technology would at least be in the TOP-3 industries. Do you think it can happen by the end of this year? submitted by /u/unbalanced_mind [link] [comments]  ( 9 min )

submitted by /u/Tao_Dragon [link] [comments]  ( 8 min )

hello everyone there was a voice-guided navigation system for GTA 4 with a female voice, I would love to use that as my Google Maps and Waze navigation voice I tried looking for anything online but the mods available are of very inferior quality and from a few years ago which won't work properly with Google Maps as a lot of the voice commands are missing the sample for the voice can be found in these 2 YouTube videos first sample 11 minutes repeated statements 2nd sample 1 minute without repeated statements Using Adobe podcast I can remove a lot of the background noise The problem is I don't know much about sound tuning so using whatever free ai I could find on the internet the voice is not being cloned properly it is sounding mechanical or just like google ttss i have a decen…  ( 10 min )

submitted by /u/Senior_tasteey [link] [comments]  ( 8 min )

submitted by /u/Vegetable_Tutor8245 [link] [comments]  ( 8 min )

Here is a draft speech focusing on the potential for AI to help create a more just and peaceful world by 2030: My fellow citizens, We stand today on the cusp of a new era. The rapid development of artificial intelligence technology presents us with an unparalleled opportunity to remake our world for the better. Though this technology poses risks, I believe that if guided responsibly, AI can help us build a society of prosperity, equity, and justice. Over the past decade, we have already seen glimpses of AI's vast potential for good. Algorithms are helping doctors diagnose diseases more accurately. Smart systems are optimizing transportation networks and reducing traffic accidents. Machine learning is enabling more efficient energy use and sustainable agriculture. But this is only the…  ( 10 min )

Seems like a strong contender in this space, plus comercial use: https://www.splashmusic.com/music-generation submitted by /u/No-Reference8836 [link] [comments]  ( 8 min )

Hello Reddit! I'm Daria from the Cerebras ML team, and I've got some thrilling updates to share with you! 🚀 We've recently published our BTLM-3B-8K paper on arXiv, and I’m excited to share that I am one of the authors! Check it out here: https://arxiv.org/abs/2309.11568 It distills our recipe for training SOTA LLMs: An extensively deduplicated dataset: SlimPajama Hyperparameter search using muP Variable sequence length training + ALiBi Aggressive LR decay Our BTLM-3B-8K not only sets a new standard for 3B parameter models but also frequently surpasses the performance of 7B models! In the paper, we meticulously outline how we developed the BTLM model and conducted a comprehensive analysis of its performance on 22 validation benchmarks. These benchmarks span a range of capabilities including common sense reasoning, world knowledge, reading comprehension, code generation, long sequence interpolation/extrapolation, bias, toxicity, and misinformation. For those eager to dive in, we've made our SlimPajama dataset and the BTLM-3B-8K model available on Hugging Face: https://huggingface.co/cerebras 🎉 Feel free to delve into the details, explore the dataset and model, and let us know your thoughts, insights, or questions! We're here to discuss and excited to hear your feedback. Happy exploring! 🚀 submitted by /u/daria-sobol [link] [comments]  ( 9 min )

Hello all, I am re-reading the LoRA paper (https://arxiv.org/abs/2106.09685) to get a deeper understanding of some of the analysis the authors perform at the end and saw this line Note that the relationship between model size and the optimal rank for adaptation is still an open question. Does anybody know of any resources out there that looked into this question, given that LoRA has been around for a little bit now? Perhaps someone has performed similar subspace overlap / optimal "r" value studies on some of the LLMs that fall in-between GPT2 and 3, i.e. some of the ~7B, ~15B, ~40B and ~70B models? submitted by /u/lightSpeedBrick [link] [comments]  ( 9 min )

If censorship isn’t an issue for me, when there’s an instruct version of an LLM, is it generally always better to use the instruct version than the non-instruct version (because instruct versions tend to hallucinate less)? Apart from censorship and hallucinations, are there any other pros and cons between intrust vs. non-instruct version? submitted by /u/--leockl-- [link] [comments]  ( 9 min )

This new paper from Shehzaad Dhuliawala et al. (2023) introduces a combination of prompting and consistency checks made by the LLM itself. Implementing this technique actually made me like gpt-3.5 again ! I wrote a tutorial on how to actually implement this method : https://advanced-stack.com/resources/how-to-reduce-hallucinations-using-chain-of-verification-cov-in-large-language-models.html Let me know if you find it useful ​ submitted by /u/Fluid-Age-9266 [link] [comments]  ( 9 min )

Hi guys, a final year CS student here. I want to create a portfolio to showcase my skills in ML and DL. I have knowledge in docker and have access to google cloud platform to deploy. Now, I am unable to find any project that stands out. Could u suggest something that I could learn from as well as looks pretty on my cv? submitted by /u/Virtual_Heron_7417 [link] [comments]  ( 9 min )

Hey everyone, I've been diving deep into the world of neural networks, and recently, I've been particularly intrigued by the dynamicity of attention head parameters (weights). These weights play a crucial role in transformers, and understanding how they change during training and inference can provide valuable insights into model behavior. The question is, what does dynamic mean in this context? Is it input-adaptive? Do weights change at inference time according to inputs? I have a hard time understanding this concept, for me, weights are static and pre-established at training time. submitted by /u/assalas23 [link] [comments]  ( 9 min )

Hey everyone, I've been diving deep into the world of neural networks, and recently, I've been particularly intrigued by the dynamicity of attention head parameters (weights). These weights play a crucial role in transformers, and understanding how they change during training and inference can provide valuable insights into model behavior. The question is, what does dynamic mean in this context? Is it input-adaptive? Do weights change at inference time according to inputs? I have a hard time understanding this concept, for me, weights are static and pre-established at training time. submitted by /u/assalas23 [link] [comments]  ( 9 min )

Hello guys! ​ Are you intrigued by the world of Artificial Intelligence? I am conducting a brief and insightful survey on AI for academic research purposes. It'll take just 2 minutes of your time, and your valuable insights will contribute to our understanding of AI trends and perspectives. Whether you're a tech enthusiast, a data wizard, or just curious about the future of AI, your input is incredibly valuable! ​ Click the link below to share your thoughts and help to understand this new technology: ​ https://forms.gle/7fbbkc1f2iBPXHJV7 ​ Thank you in advance for being a part of this exciting AI study! #AIResearch #AI #Artificial Intelligence #SurveyTime submitted by /u/JukeboxNV [link] [comments]  ( 9 min )

As AI models get bigger, training them requires more and more computing power. Researchers are looking for ways to train these large AI models without needing Google-scale resources. A new paper proposes LongLoRA, a fine-tuning approach that can extend LLaMA2 7B to 100k context length and 70B model to 32k context length on a single 8× A100 machine. Here are my highlights from the paper: Big one of course: LongLoRA efficiently fine-tunes large AI models on longer texts Key points: Approximates standard attention via "shift short attention" during training Tuning only a subset of weights (LoRA) plus some embeddings & norms Fine-tuned 7B parameter model on 100k tokens with 1 machine Way lower training cost than full fine-tuning for large contexts Close to full fine-tuning performance The core insight is that an approximation of full attention enables efficient training while retaining standard attention for final inference. Combined with selective weight tuning, this really reduces compute needs. I think this demonstrates the potential to train more capable AI without unreasonable resources. Efficient training techniques = more powerful LLMs for the same resources. Full summary here. Arxiv paper: https://arxiv.org/pdf/2309.12307.pdf submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

Hello, looking for job opportunities as a data scientist or machine learning engineer. Any help would be appreciated. Thanks!! submitted by /u/ArachnidFun2671 [link] [comments]  ( 8 min )

Denoising diffusion probabilistic models are becoming the leading paradigm of generative modeling for many important data modalities. TabDDPM is a diffusion model that can be universally applied to any tabular dataset and handles any type of feature. Blog post link. submitted by /u/metkere [link] [comments]  ( 9 min )

Context of the problem: I have the following entities: stations, programs, project manager, days, and time slots. The goal of the problem is to decide what program with who project manager to assign on what day at what time slot for each station. Some notes: Each program has its own duration. Say Program A last for 50 minutes, Program B is 30 minutes, etc. Each project manager has qualifications on what program he/she can handle. This constraint is a hard constraint. Time slots start from 6AM to 6PM. This means that if Program A which lasts for 30 minutes is assigned to start at 6:00AM, then it will end at 6:30AM. Only one program can be assigned in each station, so there should be no overlap in programs in terms of day and time per station. There is a forecasting model that takes a…  ( 10 min )

https://github.com/jaspersiebring/geococo Introducing GeoCOCO, an open source project that enables users to turn their GIS annotations (e.g. shapefile) into COCO datasets which can then be used to train computer vision models! It allows users to use the likes of QGIS, ArcGIS to annotate geographic imagery in the same way you would annotate non-geographic imagery with LabelMe. It is powered by Python and a variety of packages (e.g. pydantic, pandera, geopandas, pycocotools). On the more meta side, it also features automated tests/builds/releases through Github Actions (using the likes of poetry, ruff, mypy, pytest, black). Sharing it with you guys in case someone else might find it useful! I am also very interested to hear some feedback (suggestions, flaws, etc.), let me know! ​ Here's…  ( 9 min )

Finding linkedIn article on anomaly detection Last week I saw a LinkedIn article on anomaly detection. In that post, Tail movement of a running mice on rotating rod ( roller) is captured with CV and plotted as a time series. Then based on the tail movement time series , anomalities in that time series are detected where mice loose the balance in rotating rod. I am trying to find this post in linkedIn but still didnt able get any clue. If you have seen this research article or have any clue please let me know. It will be great help. Appriciate you time and help.Thank You very much ! ❤️ [D] [R] submitted by /u/isurusachitha [link] [comments]  ( 9 min )

I am really excited to share our newest work in deep learning: parallelizing RNN! https://arxiv.org/abs/2309.12252 RNN is thought to be non-parallelizable because of its inherent sequential nature: its state depends on its previous state. This makes training RNN for long sequence usually takes long time compared to other architecture classes (like CNN). What we present is an algorithm based on Newton's method to evaluate and train RNN in parallel. In one of our experiment, we can achieve >1000x faster evaluation of a small GRU compared to common sequential method on a very long sequence. Training RNNs with our algorithm could also be more than 10x faster than training with sequential method. However, it's not without flaws. There are 2 major drawbacks we noticed: non-convergence and sca…  ( 10 min )

Assuming using the same cloud service, Is running an open sourced LLM in the cloud via GPU generally cheaper than running a closed sourced LLM? (ie. do we pay a premium when running a closed sourced LLM compared to just running anything on the cloud via GPU?) One eg. I am thinking of is running Llama 2 13b GPTQ in Microsoft Azure vs. GPT-3.5 Turbo. I understand there are a lot of parameters to consider (such as choosing which GPU to use in Microsoft Azure etc.), but I am really looking at what’s the cheapest way to run Llama 2 13b GPTQ or a performance-equivalent closed sourced LLM. submitted by /u/--leockl-- [link] [comments]  ( 9 min )

Hi all. I am curious to know what is cutting edge in this domain? The use-case is creating an image of myself that is older than I am. I've looked a bit into StarGAN but I've never done work in this domain and don't know if this is still used today. Any help would be appreciated! submitted by /u/ProudOwner_of_Fram [link] [comments]  ( 9 min )

I am reading the following paper about self-supervised learning. ​ https://preview.redd.it/g4bz7wjxarpb1.png?width=1323&format=png&auto=webp&s=9f923825b57d4d3da346bb1bb4dd008366493dbd Briefly their idea for self-supervised learning is to reconstruct a corrupted image (e.g., random masking) using Langevin step of a learned energy function and the reconstructed image is compared to the clean image for supervision. i.e., ​ https://preview.redd.it/q9e1pd9yarpb1.png?width=953&format=png&auto=webp&s=528e654d3ae4db1435aa3c11e5edc787876fd9ac This should work because going along the decreasing energy value ensures that corrupted images have high energy and clean images have low energy. Everything made sense to me until I tried to implement it. ​ In order to optimize the parameters (\theta), we have to take the gradient of the loss w.r.t \theta and by the chain the chain rule we will compute the gradient of the reconstructed x (\tilde x) w.r.t \theta. ​ https://preview.redd.it/j3qbyddzarpb1.png?width=1280&format=png&auto=webp&s=c03d54dbaf70ba588c2a4d87691d71184797ae3f Is this even correct? What am I even talking about I am confused. Anyways ... They provided the following PyTorch pseudocode and I have provided the actual PyTorch code. ​ https://preview.redd.it/m4wd7pq2frpb1.png?width=1266&format=png&auto=webp&s=d5febf258f0c92fc90116a996367b605cd597128 Actually the model parameters never change no matter what what the values of step size (alpha) or the learning rate are. I am missing something? ​ https://preview.redd.it/yznnuws9frpb1.png?width=1185&format=png&auto=webp&s=1ced5a213d372f309e6c20a9781ac35c1abcc436 Any help is appreciated ​ submitted by /u/ThoughtOk5558 [link] [comments]  ( 9 min )

In this blog post, we illustrate how RLHF can be performed on Amazon SageMaker by conducting an experiment with the popular, open-sourced RLHF repo Trlx. Through our experiment, we demonstrate how RLHF can be used to increase the helpfulness or harmlessness of a large language model using the publicly available Helpfulness and Harmlessness (HH) dataset provided by Anthropic. Using this dataset, we conduct our experiment with Amazon SageMaker Studio notebook that is running on an ml.p4d.24xlarge instance. Finally, we provide a Jupyter notebook to replicate our experiments.  ( 11 min )

Here’s a quote from Don Zagier that I found in Larry Rolen’s lecture notes on modular forms. There are two facts about the distribution of prime numbers of which I hope to convince you so overwhelmingly that they will be permanently engraved in your hearts. The first is that, despite their simple definition and role […] Primes, weeds, and military precision first appeared on John D. Cook.  ( 5 min )

A continued fraction of the form with n terms can be written as the composition where As discussed in the previous post, a Möbius transformation can be associated with a matrix. And the composition of Möbius transformations is associated with the product of corresponding matrices. So the continued fraction at the top of the post […] Continued fractions as matrix products first appeared on John D. Cook.  ( 5 min )

A function of the form where ad – bc ≠ 0 is sometimes called a fractional linear transformation or a bilinear transformation. I usually use the name Möbius transformation. In what sense are Möbius transformations linear transformations? They’re nonlinear functions unless b = c = 0. And yet they’re analogous to linear transformations. For starters, […] Fractional linear and linear first appeared on John D. Cook.  ( 6 min )

submitted by /u/shani_786 [link] [comments]  ( 8 min )

Hi everyone, I'm a newbie for the domain of reinforcement learning (RL). My main research on various software systems, mainly on multi-agent system (MAS). AAMAS, a conference focusing on Multi-agent system, I think is the good conference to study in MAS. Recently, there are many researchers for MAS that is great combination with reinforecement learning to do some interesting things. Also, game theory is a interesting knowledge what I'm curious about how to do within RL and agents. Does any recommended conference or journal for RL? don't mind for just only RL algorithm, that's enough great, whereas I prefer to the conference concerning RL applied some software system for some problem. Thanks all. submitted by /u/DryAir1198 [link] [comments]  ( 9 min )

trying to make a frozen lake game but keep getting this weird error and i dont know how to fix it. The error is "too many values to unpack (expected 4)" on the line: next_state, reward, done, _ = env.step(action) import gym import numpy as np import pygame from pygame.locals import QUIT import tensorflow as tf from tensorflow import keras import warnings def ignore_specific_warning(): warnings.filterwarnings("ignore", message="This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.") ​ ignore_specific_warning() ​ input_size = env.observation_space.n output_size = env.action_space.n ​ model = tf.keras.Sequential([ tf.keras.layers.Dense(64,activation='relu',input_shape=(input_size,),use_bias=False), tf.keras.layers.Dense(output_size,use_bias=False)]) ​ loss_fns = tf.keras.losses.MeanSquaredError() optimizer = tf.keras.optimizers.Adamlearning_rate=0.001 ​ discount_factor=0.99 learning_rate=0.1 num_episodes=1000 ​ for i in range(num_episodes): state = env.reset() done = False while not done: env.render() epsilon = 0.1 if np.random.rand() < epsilon: action = env.action_space.sample() else: #Q_values = model.predict(tf.one_hot(state[0],input_size)) #action = np.argmax(Q_values) state_one_hot = tf.one_hot(state[0], input_size) Q_values = model.predict(tf.reshape(state_one_hot, (1, -1))) action = np.argmax(Q_values) next_state, reward, done, _ = env.step(action) target = reward*discount_factor*np.max(model.predict(tf.one_hot(next_state,input_size))) with tf.gradientTape() as tape: Q_values = model(tf.one_hot(next_state,input_size)) loss = loss_fn(Q_values[0][action],target) gradients = tape.gradients(loss,model.trainable_variables) optimizer.apply_gradients(zip(gradients,model.trainable_variables)) state = next_state env.close() submitted by /u/BeastHunterrr [link] [comments]  ( 9 min )

submitted by /u/nickb [link] [comments]  ( 8 min )

submitted by /u/nickb [link] [comments]  ( 8 min )

My father, a retired Computer Science professor and pioneer in network design algorithms, has asked me to post two questions he has as he attempts to teach himself about neural networks. For physical/medical reasons he can’t post them himself. Grateful for any answers or suggestions for where he could find them! His note: I have many questions, but they all revolve around two issues. The first is when the weights in the neural net converge, do they exhibit any recognizable pattern. I realize that when the number of nodes and levels in the net are large it may be impossible to recognize the pattern. But suppose that the problem being examined is a simple categorization and the number of nodes in the net is small, is it possible to see a pattern in the weights? The network is supposed to …  ( 10 min )

submitted by /u/Raymondlkj [link] [comments]  ( 8 min )

submitted by /u/Raymondlkj [link] [comments]  ( 8 min )

submitted by /u/Raymondlkj [link] [comments]  ( 8 min )

submitted by /u/Raymondlkj [link] [comments]  ( 8 min )

I am looking for a way that I can take a bunch of saved voicemails from my mom and be able to hear her voice again. It would mean the world to my family and if my kids could hear her voice I know it would brighten their day. Can anyone point me in the right direction to accomplish this? submitted by /u/blbjtb [link] [comments]  ( 9 min )

The US Copyright Office has ruled that AI-generated art cannot be copyrighted, raising questions about whether AI-generated art should be excluded from copyright protection. The Copyright Office argues that AI-generated art is a 'merely mechanical' process with no novelty, invention, or originality, and therefore does not deserve copyright protection. Critics, however, argue that this approach is not scalable and fails to consider the creative choices made by AI artists. They suggest that AI-generated art should be treated similarly to photography, where even though the image is captured mechanically, it still reflects the creative choices of the photographer and is eligible for copyright protection. Photographers are able to own the copyright in their photographs because they make creative judgments about where to point the camera, when to snap the image, and how to adjust camera settings. Similarly, AI artists explore the 'latent space' of images that AI software can produce, making creative judgments about which images to select and explore. While the actual image is produced by the software, the important point is that a human being is making creative decisions about the art. Therefore, critics argue that AI-generated art should be eligible for copyright protection, as it reflects the creative choices and judgments of the AI artist. Source : https://www.understandingai.org/p/dont-exclude-ai-generated-art-from submitted by /u/NuseAI [link] [comments]  ( 9 min )

I'm looking for a free chatbot service that allows base prompt customization and offers API access (so I can dynamically change the stock on hand to better assist customers, for example). I looked into https://ora.ai, but it seems you can only set the base prompt once and manually. I'm hoping to find a service that allows for more flexibility. The service must also be free and offer embedding, like https://ora.ai/, (with div) submitted by /u/LimeLom1 [link] [comments]  ( 9 min )

Consciousness theory slammed as ‘pseudoscience’ — sparking uproar (Nature) The irony here is that I mostly agree with this theory - but the article reflects how little we really know about consciousness and how it works, and how what's considered the "expert opinion" that AI can't possess consciousness is arguably influenced more by popularity than real empirical evidence. By whatever mechanism, they can respond to their treatment in unexpectedly humanlike ways. Oh, and by the way, did you think that "sentient Bing" was finally dead? Think again. submitted by /u/kamari2038 [link] [comments]  ( 9 min )

submitted by /u/Hazzman [link] [comments]  ( 8 min )

I was thinking of signing up to VoicifyAi for fun a create some custom covers but are there better (or even free) alternatives? submitted by /u/quantummufasa [link] [comments]  ( 8 min )

Edit: FLAC is the tested audio extension, not MP3 I read the new paper from DeepMind so you don't have to. Here are the key highlights: Despite training on text, langauge models compressed images 43% better than PNG, and audio nearly 2x better than flac. Confirmation of scaling laws - bigger models compressed better. But model size must match dataset size. There are tradeoffs between model scale, data size, and compression performance. More data enables bigger models. Tokenization (like BPE) generally hurts compression slightly by making prediction harder. Longer contexts let models exploit more sequential dependencies. Implications: Models have learned very general capabilities beyond just text. Their strong compression reflects deep understanding of images, audio etc statistically. I got some new perspective on model scaling laws and links between prediction and generalization. There's potential for practical applications compressing images, video etc. But large model size an issue. Overall it shows these models are very capable general purpose learners, not just for language. Full summary here if you want more details. Original paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

This AI-generated art style is gaining a lot of traction on the internet. So I wanted to share how you can create art like this in under 2 minutes: • Visit the Fusion Art website and sign up for free (https://quickqr.art/app/fusion-art) • Choose a template or upload your reference template image. • Describe your image to tailor the output. • Tweak the aspect ratio and explore added features. • Hit "Generate" That's it! Share your art to showcase what you come up with. Hope this was helpful for anybody looking to create this style of art! https://preview.redd.it/j3o9puk9smpb1.png?width=1536&format=png&auto=webp&s=ddbe1328bd19dc89c4cd82ef1870b9de695e5500 https://preview.redd.it/9ppxjpeasmpb1.png?width=768&format=png&auto=webp&s=29dd845eb5f8f094abe5bc1135060965f51365cf https://preview.redd.it/58vjky3bsmpb1.png?width=1536&format=png&auto=webp&s=06db1926e021ea3f2c438fb723c453758a461c43 https://preview.redd.it/w2nvaoobsmpb1.png?width=1024&format=png&auto=webp&s=0b4bcaea4fc7c832bbce9f7f3b9319099df59a14 submitted by /u/IndifferentSpectat0r [link] [comments]  ( 9 min )

submitted by /u/Vinitneo [link] [comments]  ( 8 min )

submitted by /u/daveNZL [link] [comments]  ( 8 min )

Hello, Reddit! At its core, Prompt Engineering is all about commanding the conversation with AI. Wanna write a poem, draft a project plan, or break down complex scientific terms? The right prompt can do that for you. If you want to learn more about Prompt Engineering feel free to join r/PromptWizards. Let's dive into some Prompt Engineering concepts: Let's start with the basics: Reductive operations: These yield responses that are smaller in size than the input prompt. Essentially, they distill information. Example: If the input prompt is a long article, and the task is to provide a summary, the operation used is reductive as the output (summary) is smaller than the input (the entire article). Transformational operations: These make sure the output maintains a relative balance w…  ( 10 min )

ChatGPT Plus: The best use of AI for humanity is to augment human capabilities, address pressing global challenges, and improve the quality of life for all. By analyzing vast amounts of data at unprecedented speeds, AI can aid in medical research, predict and mitigate natural disasters, optimize energy consumption, and personalize education to individual needs. Moreover, it can provide solutions to complex problems, such as climate change, resource scarcity, and public health crises, ensuring a more sustainable and prosperous future for generations to come. Bard: The best use of AI for humanity is to use it to solve problems that will make the world a better place for everyone. AI has the potential to improve our lives in many ways, such as developing new and more effective ways to diag…  ( 10 min )

Hugging Face CEO Clem Delangue says open-source AI was vital to starting his company. Now, he finds himself defending open AI models as Washington considers new regulations. On POLITICO Tech, Delangue explains why he views open-source AI as not only safe, but necessary to prevent big tech companies from gaining more market power. Listen for more: https://politico-tech.simplecast.com/episodes/the-hugging-face-case-for-open-ai submitted by /u/smo279 [link] [comments]  ( 9 min )

I have a product and I need product photos. It is a bottle and I am wanting ai to make a realistic picture of a woman holding the bottle and smiling. Can this be done? If so, what software/website/app do I use for this? Thank you submitted by /u/Ok_Salt_9211 [link] [comments]  ( 8 min )

submitted by /u/Senior_tasteey [link] [comments]  ( 8 min )

75% of Americans believe that AI will decrease the total number of jobs in the next 10 years, according to a survey by Bentley-Gallup Business in Society study. Younger Americans are less pessimistic about AI compared to older age groups. Majorities believe that AI performs as well as or better than humans in customizing online content, recommending products or services, and assisting students with coursework. However, Americans are skeptical about AI's ability to recommend employees, its self-driving capabilities, and its ability to recommend medical advice. 79% of Americans have little trust in businesses to use AI responsibly. 40% of Americans think AI does more harm than good, while only 10% believe it contributes more good than harm. Black and Asian Americans have a more positive view of AI's impact on society compared to Hispanic and White Americans. While most Americans are wary of AI's impact on the job market, younger people are more optimistic about its future. Businesses need to affirm their commitment to using AI responsibly and address the knowledge deficit and lack of confidence among Americans. Source : https://news.gallup.com/opinion/gallup/510635/three-four-americans-believe-reduce-jobs.aspx submitted by /u/NuseAI [link] [comments]  ( 9 min )

OpenAI unveils DALL-E 3, allows artists to opt out of training.[1] Infosys, the Indian tech giant, has announced a strategic partnership with NVIDIA, a leading provider of enterprise-grade AI solutions. The collaboration aims to empower enterprises and businesses with generative AI-based solutions that drive productivity.[2] Alibaba and Tencent Invest in Chinese State-Backed Zhipu AI.[3] John Grisham, George R.R. Martin and more authors sue OpenAI for copyright infringement.[4] Sources: [1] https://techcrunch.com/2023/09/20/openai-unveils-dall-e-3-allows-artists-to-opt-out-of-training/ [2] https://gameishard.gg/news/infosys-and-nvidia-partner-to-deliver-generative-ai-solutions/205456/ [3] https://winbuzzer.com/2023/09/20/alibaba-and-tencent-invest-in-chinese-state-backed-zhipu-ai-xcxwbn/ [4] https://apnews.com/article/openai-lawsuit-authors-grisham-george-rr-martin-37f9073ab67ab25b7e6b2975b2a63bfe submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

When AI takes all the data for training purposes without feedback to UGC platforms, could this eventually lead to the demise of UGC platforms, and could the internet become increasingly closed until it collapses? submitted by /u/oodzchen [link] [comments]  ( 9 min )

Asked the new Canva AI to blur my image. It Blurred my image. submitted by /u/MDINOKC [link] [comments]  ( 8 min )

The Awk programming language was named after the initials of its creators. In the preface to a book that just came out, The AWK Programing Language, Second Edition, the authors give a little background on this. Naming a language after its creators shows a certain paucity of imagination. In our defense, we didn’t have a […] Naming Awk first appeared on John D. Cook.  ( 4 min )

I’d love to see people come together, donate their spare compute to train more open source models, boost research etc. One relevant paper I was able to find is this one https://arxiv.org/abs/2103.08894 submitted by /u/tecbar [link] [comments]  ( 8 min )

I'm planning on submitting a rejected NeurIPS paper to the AAAI fast track. A few days ago I registered myself as an author on CMT, but I cannot see an option to submit a paper. Will the portal open later? Can anyone else see an option to submit yet? submitted by /u/Firm-Act-3860 [link] [comments]  ( 9 min )

This year will be my first NeurIPS, and I see some cool workshops but don't have any work that would be a good fit for them. Can I just go and listen? submitted by /u/ThickBiker [link] [comments]  ( 8 min )

So a few weeks ago I purchased a Sega 36 Crane Machine (Claw Machine) off ebay and everything worked great except the claw strength being too strong. I took the board out so I could possibly have something added to the board for me. It was missing a “Free Play” button which is used to adjust claw strength on this machine.. Anyway, I kept the board in the garage while searching for the right parts and dad threw it away on accident while cleaning, now im out a board and I have no idea what to do. Can anybody help me or am I sol? Even if im in the wrong community for this, a step to the right direction would even help. I also have attached some pictures of the board.. Im not good with these boards at all and have no clue what im looking for. Theres also a diagram of the boards functions! Thanks for all information/help! submitted by /u/Ready_Highlight9758 [link] [comments]  ( 9 min )

Hi, I am an experienced trader and coder. I am embarking on a journey to develop an open-source Trading AI in Python, and I'm looking for passionate individuals to join me in this project. This is a non-commercial, community-driven project, so there won't be any monetary compensation involved. However, it's a great opportunity to learn and collaborate in the field of trading AI. ​ - You don't need to know trading to contribute. - As an Open Source project, you have complete freedom to use the AI. ​ About the Project: - Objective: Our goal is to create a self-learning AI system for trading in financial markets. - Tech Stack: We'll be working with Python for data analysis, machine learning, and neural network development. - Data: We'll be using historical stock market data to train a…  ( 9 min )

Hi, we're welcoming talented Machine Learning Engineers with a focus on Recommendation Systems and ML Operations. Who are we? Leading in VR video streaming, we drive the DeoVR platform with interactive 8K videos, ML, computer vision, and advanced haptic tech. What you'd do? For MLE RecSys: develop ML pipelines for top recommendations, engage with community for desired features, and collaborate with Backend/Frontend/Unity teams. For ML Ops: construct ML inference infrastructure, refine models & workflows, and ensure high-availability ML services. What we offer? 🌍Remote flexibility 🤝Collaborative and inclusive work environment 🚀Make a significant impact in the VR industry Interested? Check out and apply here or drop me a DM😎 submitted by /u/SanjaVR [link] [comments]  ( 9 min )

i.e., models with the exact same architecture, but their initial weights are different, and the order which the model sees the training data is different. submitted by /u/just2gud [link] [comments]  ( 8 min )

We are happy to share with you BlindChat, the open-source and privacy-by-design alternative to ChatGPT for a fully in-browser, yet private, Conversational AI! You can play with it on our Gradio demo. Our philosophy is that Conversational AI should be easily accessible, and privacy should not be a luxury but a given. By leveraging local models running in the browser, with transformers.js, we make it possible to have a fully transparent and private AI that works on your browser without any extra setup. Because all the logic is offloaded to users’ devices, data never leaves and there is no risk of it being used for finetuning by third parties. For now, we only support inference with LaMini-Flan-T5, so you might see modest performance. We plan to integrate Microsoft phi-1.5 for better performance once the 370M is out. We are also working on LlamaIndex-TS integration on the client side to have in-browser RAG for local querying of private documents. As our roadmap is quite dense, with RAG, internet search, improved inference, we welcome warmly contributors! If you want to contribute, or have questions, ping us on Discord and GitHub! submitted by /u/Separate-Still3770 [link] [comments]  ( 9 min )

Edit: FLAC is the tested audio extension, not MP3 I read the new paper from DeepMind so you don't have to. Here are the key highlights: Despite training on text, langauge models compressed images 43% better than PNG, and audio nearly 2x better than flac. Confirmation of scaling laws - bigger models compressed better. But model size must match dataset size. There are tradeoffs between model scale, data size, and compression performance. More data enables bigger models. Tokenization (like BPE) generally hurts compression slightly by making prediction harder. Longer contexts let models exploit more sequential dependencies. Implications: Models have learned very general capabilities beyond just text. Their strong compression reflects deep understanding of images, audio etc statistically. I got some new perspective on model scaling laws and links between prediction and generalization. There's potential for practical applications compressing images, video etc. But large model size an issue. Overall it shows these models are very capable general purpose learners, not just for language. Full summary here if you want more details. Original paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

Hi, I am interested in taking couple of AI/ML online courses from the Stanford's Artificial Intelligence Professional Program (https://online.stanford.edu/programs/artificial-intelligence-professional-program). I am interested in taking XCS221 and XCS229. My employer would be paying for this (I hope!). I know these are not the complete courses offered in the university. Has anyone taken courses from this program ? Is it worth it ? submitted by /u/RealMadrista007 [link] [comments]  ( 9 min )

This Twitter thread claims that OpenAI's new language model gpt-3.5-turbo-instruct can "readily" beat Lichess Stockfish level 4. This tweet shows the style of prompts that are being used to get these results with the new language model. I used website parrotchess[dot]com (discovered here) to play multiple games of chess purportedly pitting this new language model vs. various levels of Fairy-Stockfish 14 at website Lichess. My current results for all completed games: The language model is 2-0 vs. Fairy-Stockfish 14 level 5 (game 1, game 2), and 0-2 vs. Fairy-Stockfish 14 level 6 (game 1, game 2). One game I aborted because the language model apparently tried an illegal move. The following is a screenshot from the aforementioned chess web app showing the end state of the first game vs. Fai…  ( 9 min )

I've been playing around with transformers since following Karpathy's shakespeare model on youtube and found it really cool. Thought I should write a Tree-of-Thoughts model as my next project which will be somewhat similar, given what we're doing is learning the meaning behind words and phrases and then using that knowledge to generate a sequence based on a seed token or sequence of tokens. Here's how I was thinking it should go: I write a next token in sequence prediction model and write a generate function that generates a sequence of tokens on top of a seed token. Then I write a tree model that takes in whatever this bigram has learned and adds onto it some kind of knowledge about where one thought about the seed has been generated and stop there. Then iteratively generate a few more such thoughts based on the same seed. This is how I initially thought it would go but now when I'm writing, I've already written the bigram(next token prediction and generation), I don't know what to do next. I am kind of stuck. It will be interesting to see how you, if you've ever written a similar model or are good at NLP, would attempt this. Looking at a variety of different approaches to the same problem I am trying to solve would be great. So I decided to post here. Would be great to hear from you guys! submitted by /u/GraphicsMonster [link] [comments]  ( 9 min )

Press release Diffusion Policy: Visuomotor Policy Learning via Action Diffusion (contains link to paper) Comments on Hacker News with some interesting info / links. submitted by /u/falconberger [link] [comments]  ( 8 min )

Hi all, I have found this dataset on Kaggle with zero information about it. It is very intriguing and would like to find the original researchers about it. It contains over 270,000 histopathological images of cervical cancer. I cannot contact the person who posted it on Kaggle because I am not a contributor yet. And they are not replying on other platforms. Any help on finding it will be very much appreciated. If not, where else can I get such large cervical or bladder cancer datasets? Link to the dataset mentioned: https://www.kaggle.com/datasets/rzelite/cervical-cancer Thanks in advance. submitted by /u/dumb_persn [link] [comments]  ( 9 min )

https://preview.redd.it/u7lq37efllpb1.png?width=1029&format=png&auto=webp&s=824ee138d9ae4b28d1600b969eab2077f47fb2a6 GitHub CEO Thomas Dohmke spoke on stage at TC Disrupt today and made several statements regarding the development of artificial intelligence and programming: Dohmke believes that AI won't replace software developers but will make them more efficient. Despite AI advancements, the demand for software developers will continue to exceed the supply. The growth in software usage is expected to be exponential, with every company becoming a software company. Legacy code maintenance remains crucial, especially in industries like finance with outdated codebases. Generative AI is creating more demand for technical talent as companies seek to adopt innovative AI solutions. There is a shortage of computer science students, leading to increased demand for developers with AI skills. Source: https://techcrunch.com/2023/09/20/github-ceo-despite-ai-gains-demand-for-software-developers-will-still-outweigh-supply/ submitted by /u/gcore-com [link] [comments]  ( 9 min )

Hello everyone I have a doubt regarding GANs for tabular data, more specifically for time-series generation. Looking at the TimeGAN paper (code) and other implementations such as the one by YData, considering that the training dataset is divided into slices (like a rolling window) of N length and that the generated sequence will follow the same format, that is [batch size, N, n_features], what we are effectively generating are slices and not a fully synthetic time-series. ​ To clarify my question, the output of a TimeGAN is only a set of slices, although there is no guarantee that generated slice(N-1) is the one before slice(N), and so the true use of a TimeGAN is generating slices that can be used to train a predictive TS model but not reconstructing a complete time series like the one used for training. ​ I suspect that's the case (useful only to generate good slices for training predictive models) as there is no fully generated time series in the paper or any of the codes, while the quality of the generated data is partially measured by training a model on original data and generated data and comparing their predictive performance. If anyone can confirm/develop upon these thoughts I would be extremely grateful. submitted by /u/iReallyReadiT [link] [comments]  ( 9 min )

I've been working on our interactive dataset explorer for machine learning: renumics-spotlight. Recently, I set up an image classification example using it. I utilized Bing image search to create a fully functional example for custom-defined classes. To my surprise, it not only worked efficiently but also took only a few minutes for fine-tuning. The best part is its adaptability. You can easily switch it up for different image classes. Maybe you like it. Let me know what you think or if you have any suggestions to improve! Install with pip install renumics-spotlight sliceguard[all] Imports from renumics import spotlight from sliceguard.data import create_imagedataset_from_bing from sliceguard.models.huggingface import finetune_image_classifier, generate_image_pred_probs_embeddings from sliceguard.embeddings import generate_image_embeddings ​ Create an Image Dataset from Bing class_names = [ "Blue Tang", "Clownfish", "Spotted Eagle Ray", "Longnose Butterfly Fish", "Moorish Idol", "Royal Gramma Fish", ] df = create_imagedataset_from_bing( class_names, 25, "data", test_split=0.2, license="Free to share and use" ) Fine-tune a ViT Model with the data (in 1-2 minutes on a GPU) finetune_image_classifier( df[df["split"] == "train"], model_name="google/vit-base-patch16-224-in21k", output_model_folder="./model_folder", epochs=15, ) Enrich the DataFrame with Predictions, Probabilities and Embeddings and visualize it: df["prediction"], df["probs"], df["embeddings"] = generate_image_pred_probs_embeddings( df["image"].values, model_name="./model_folder" ) # Check the result and detect problematic clusters spotlight.show( df, layout="https://spotlight.renumics.com/resources/image_classification_v1.0.json" ) ​ https://i.redd.it/20qy5xw62lpb1.gif submitted by /u/DocBrownMS [link] [comments]  ( 9 min )

Hi there, due to the lack of my resources, I have to use quantized big-model or something lighter. In this situation, I want to try the first option, and goona finetune some model. How do you expect the result? Training huge model in 4-bits circumstance will be significantly different from original setting? Thanks. submitted by /u/Mundane_Definition_8 [link] [comments]  ( 9 min )

Paper: https://doi.org/10.48550/arXiv.2006.10562 Hi all, This paper explores the use of using a single model (meaning an ensemble of trees) to generate uncertainty. This technique has been implemented into catboost. My question is why hasn't this been implemented into xgboost? The technique looks easily applicable but I would have expected it to be implemented already as it is 2 years old. Is this for some reason not applicable to Xgboost? ​ Figure 1 from paper showing the 'virtual' ensemble submitted by /u/MetalOrganicKneeJerk [link] [comments]  ( 9 min )

Previously I was spoiled my LangChain. Suppose I have a bunch of PDFs that I want to store as context vectors. And I want to use an open-source LLMs. Without using LangChain, how do I generate the context vectors? (I will store it in vector databases) submitted by /u/stephenhky [link] [comments]  ( 9 min )

For those who missed it: DALL-E 3 was announced today by OpenAI, and here are some interesting things: No need to be a prompt engineering grand master - DALL-E 3 enables you to use the ChatGPT conversational interface to improve the images you generate. This means that if you didn't like what it produced, you can simply talk with ChatGPT and ask for the changes you'd like to make. This removes the complexity associated with prompt engineering, which requires you to iterate over the prompt. Majure improvement in the quality of products compared to DALL-E 2. This is a very vague statement provided by OpenAI, which is also hard to measure, but personally, they haven't failed me so far, so I'm really excited to see the results. DALL-E 2 Vs. DALL-E 3, image by OpenAI From October, DALL-E 3 will be available through ChatGPT and API for those with the Plus or Enterprise version. And there are many more news! 🤗 I've gathered all the information in this blog 👉 https://dagshub.com/blog/dall-e-3/ submitted by /u/RepresentativeCod613 [link] [comments]  ( 9 min )

Posted by Cheng-Yu Hsieh, Student Researcher, and Chen-Yu Lee, Research Scientist, Cloud AI Team Large language models (LLMs) have enabled a new data-efficient learning paradigm wherein they can be used to solve unseen new tasks via zero-shot or few-shot prompting. However, LLMs are challenging to deploy for real-world applications due to their sheer size. For instance, serving a single 175 billion LLM requires at least 350GB of GPU memory using specialized infrastructure, not to mention that today's state-of-the-art LLMs are composed of over 500 billion parameters. Such computational requirements are inaccessible for many research teams, especially for applications that require low latency performance. To circumvent these deployment challenges, practitioners often choose to deplo…  ( 93 min )

Hi world I'm trying to implement dqn and ddqn on various playgrounds with Matlab, from scratch. I use dqn and ddqn with replay buffer. I tried to used gradient clipping. But I find it very difficult to tune. I mean I tuned and programmed successfully various algorithm such as different GAN that are not necessarily very easy to tune. RL algorithm seems even worse. I tried several grid world problems, cart pole and even a very simple second order system regulation (integrator + 1 st order pôle). Even the Matlab cart pole demo with ddqn does not provide a very satisfactory result (after training, the contrôler keep the pendulum in uprigth position, but the cart slowly drift). On the second order system, the learning seems ok for about 200 épisode(test shows that the expected behavior is almost learnt) and then suddenly the average episode score has a kind of inflection and everything blowns up, despite a very large replay buffer. So my question : is it possible to make dqn and ddqn work well with a reasonable tuning session length? Are policy optimization easier to tune (and/or more efficient)? So far, it seems to me that these action-vzlue based algorithms are highly unstable and the training may only works for a 'miraculous' tunning. What are you thought? submitted by /u/seb59 [link] [comments]  ( 9 min )

Hello there, I'll provide a quick introduction first. I am a mechatronics engineer student who is graduating this semester, I have been also privately studying ML, DL, CV for the past year because that's when I decided that I want to make an autonomous drone as my thesis/graduation project and oh my god, little did I know. I have aquired so much knowledge in the data science field and truth be told I love it, it engages my mind like crazy so I've decided that i would like my career to be revolved around electronics and programming/ data science. Back to the topic, I am working on object detection (I got it figured out), and reinforcement learning, so what I would like to do is to train the model on reaching it's destination using collision avoidance, I have a PIX4, RPI 4B 4GBs since I'm also building my drone from scratch, so let's say I have all the hardware which ofcourse includes the GPS + Compass and 5 ultrasonic sensors, 1 at each side and 1 down to hold the altitude accurately. I was thinking of HITL, making a virtual environment to train my model there because this is what logic says, ofcourse( I could also fly it using the RC controller and for example test if the obstacle avoidance is going to overrule the manual drive, which is also my objective). The question is: how can I make sure virtual drone is going to be equipped with the real-life sensors and in the needed positions? How can I feed all the sensors data during the training? Which type of reinforcement learning models should I implement? I just feel like I dragged myself into an incredibly amazing and complex project which is going to end up fucking my semester and I won't be able to graduate, it's just that my stress now is skyrocketing. Please feel free to throw any advice or opinions my way, and thank you for keeping up with this long post. 😁 submitted by /u/Gabii99 [link] [comments]  ( 10 min )

I am using Game Maker and I just wanted to check if this is how to do the cost function. ​ loss_function = 0 derivative_gradient = 0 array_copy(global.main_inputs,0,global.inputs,0,array_length(global.inputs)) for (var i = 0; i < array_length(buffer_sampling); i++) { var _reward = buffer_sampling[i][2] global.inputs = buffer_sampling[i][3] var _max_q = forward_prop_t()*global.gamma var yi = _reward + _max_q var cur_q_value = buffer_sampling[i][1][0] loss_function += power((yi - cur_q_value),2) derivative_gradient += 2*(cur_q_value - yi) } array_copy(global.inputs,0,global.main_inputs,0,array_length(global.main_inputs)) derivative_gradient = derivative_gradient*(1/array_length(buffer_sampling)) global.cost = loss_function*(1/array_length(buffer_sampling)) ​ basically, buffer_sampling is an array with tuples of [first_state, [q_value,action], reward, next_state] Thanks for any help! submitted by /u/Daninjacat256 [link] [comments]  ( 9 min )

I am trying to build intuition on why the actor update equation actually help the actor improving its policy. From what I understand, the update is proportional to the Critic's TD error. A better than expected reward will lead the actor to increase the prob of taking the associated action, and a disappointing reward will make the actor to move away from the given action. Here's where I feel like I'm missing a piece of the puzzle. Between a good state/action pair accurately valued by the Critic and a bad state/action pair undervalued by the Critic, the actor's update will favor the bad action more. Could we not conceive a scenario in which the agent gets stuck in a suboptimal policy because of that particular behavior? What triggers this questioning is that I have implemented a standard AC algo for Pixelcopter and found my agent getting stuck easily on a suboptimal policy (i.e. constantly getting higher, or lower until crash, while the critics assign good values on center squares and bad values to squares close to the wall's edge). For TD0, this is pretty marginal, but gets more significant for n-step TD as n increases. Any thoughts? submitted by /u/infundibuliforme [link] [comments]  ( 9 min )

Hello machine and reinforcement learners! This is an announcement and call for participation in the League of Robot Runners, a new 🚀 competition and research initiative 🚀 that tackles one of the most challenging problems in industrial optimisation: Multi-Robot Path Planning (sometimes also called Multi-Agent Path Finding). Recently launched at ICAPS 2023, the competition is inspired by a variety of new and newly emerging applications that rely on mobile robotics 🦾🤖. For example, Amazon automated warehouses, where up to thousands of robots work together to ensure safe and efficient package delivery 🧸📦 🚚 ❤️. Participants in the competition are asked to compute coordinated and collision-free movement plans ⤴️ ➡️ ⤵️ 🔄 for a team of robotic errand runners. Get the robots to their d…  ( 10 min )

In this post, we discuss how United Airlines, in collaboration with the Amazon Machine Learning Solutions Lab, build an active learning framework on AWS to automate the processing of passenger documents. “In order to deliver the best flying experience for our passengers and make our internal business process as efficient as possible, we have developed […]  ( 10 min )

To add to our guidance for optimizing deep learning workloads for sustainability on AWS, this post provides recommendations that are specific to generative AI workloads. In particular, we provide practical best practices for different customization scenarios, including training models from scratch, fine-tuning with additional data using full or parameter-efficient techniques, Retrieval Augmented Generation (RAG), and prompt engineering.  ( 10 min )

The NVIDIA Studio laptop lineup is expanding with the new Microsoft Surface Laptop Studio 2, powered by GeForce RTX 4060, GeForce RTX 4050 or NVIDIA RTX 2000 Ada Generation Laptop GPUs, providing powerful performance and versatility for creators.  ( 8 min )

Gone are the days when AI was the domain of sprawling data centers or elite researchers. For GeForce RTX users, AI is now running on your PC. It’s personal, enhancing every keystroke, every frame and every moment. Gamers are already enjoying the benefits of AI in over 300 RTX games. Meanwhile, content creators have access Read article >  ( 8 min )

For seasoned 3D artists and budding digital creation enthusiasts alike, an alpha version of the popular 3D software Blender is elevating creative journeys.  ( 7 min )

NVIDIA founder and CEO Jensen Huang will highlight the newest in generative AI and cloud computing at the NVIDIA AI Summit in Tel Aviv from Oct. 15-16. The two-day summit is set to attract more than 2,500 developers, researchers and decision-makers from across one of the world’s most vibrant technology hubs. With over 6,000 startups, Read article >  ( 5 min )

Time to get the gang back together — PAYDAY 3 streams on GeForce NOW this week. It’s one of 11 titles joining the cloud this week, including Party Animals. The Perfect Heist PAYDAY 3 is the highly anticipated sequel to one of the world’s most popular co-op shooters. Step out of retirement and back into Read article >  ( 5 min )

TL;DR How to build CI/CD pipelines with AWS SageMaker for ML training: https://dagshub.com/blog/ci-cd-for-continuous-training-with-sagemaker/ One of the most time-resources-energy-consuming tasks we face when we build internal projects is setting up an instance for ML training. It's a repetitive process as we run multiple experiments over the project life cycle, with many steps and configurations that are usually documented poorly and scattered around different places. As good engineers, we decided to automate the process using CI/CD pipelines. But how? We had ZERO ideas on how to do it, so we had to go through the rigorous process of using AWS guides and tutorials to figure it out. Usually, when this happens, we extend the project lifecycle and have a team member document the process so we can refer back to it when we need to do it again. Knowing this can benefit the community, we decided to share a series of blogs that guide you through building CI/CD pipelines for continuous training with AWS SageMaker. We published the first blog, which covers the configuration part a month ago, and we are happy to share the second one which explains how to build a continuous training pipeline for ML. Configure AWS SageMaker for CI/CD: https://dagshub.com/blog/setup-sagemaker-for-ci-cd-pipelines/ How to build CI/CD pipeline with AWS SageMaker for ML training: https://dagshub.com/blog/ci-cd-for-continuous-training-with-sagemaker/ I'm sure we can improve these tutorials, and would love to learn from your experience on how we can do it! 🤗 submitted by /u/RepresentativeCod613 [link] [comments]  ( 9 min )

Hi redditors! I've been tasked with an technical homework that asks me to "productionize" a Jupyter notebook. The notebook's workflow is as follows: Reads data from a CSV. Pre-processes the data. Trains a machine learning model. Makes predictions. My initial thoughts are: Separation of Concerns: Break the notebook down into distinct components - data processing, model training, and inference. Containerization: Write a Dockerfile to ensure the environment is reproducible and isolated. API for Prediction: Set up a Flask-based service to expose the model's prediction capability. However, I'm grappling with a few challenges: I'm not seeing a stark difference between the Jupyter notebook setup and the production setting. Becuase the model is small and training is not complicated. If I were to deploy to Kubernetes pods, it seems wasteful that many pods are doing the same thing( prediction for the same model, maybe just as high availability guarantee?) . Does the training phrase or data cleansing phase need to be containerized and deployed? Because this seems like a one-off process. How to deploy a scalable service? I am new to this. Each container may have a HTTP service. How to load balance them on Kubernetes from outside? What kind of CI/CD do you recommend for this task? What kind of testing or pipeline are needed? submitted by /u/zjplab [link] [comments]  ( 9 min )

Hi r/MachineLearning After a long beta, we are excited to announce Zivy Scholar to the public! Zivy Scholar is a tool that allows you to help researchers consume research papers. I'm one of the creators and I've found that I want to listen to research papers in the car or when I'm working out. Current features include: Listen to and read along with a research paper Share the paper with colleagues and friends. We use state of the art pdf data extraction techniques with TTS to provide this functionality. Features we're planning: Full PDF to HTML including images, tables, and figures inline for the read-along experience. This includes mobile optimization. This functionality is based on some newer research and we're excited to bring it to you all! Discussion and feedback are welcome! Cheers, Collin submitted by /u/collin_code_77 [link] [comments]  ( 9 min )

The title basically conveys the entire message. Which according to you is the best AI model for generation of code. I mainly code in python with AI and deep learning as a core focus and recently started doing a bit of c++ and thus far have used only bard and ChatGPT 3.5. With this experience of my I can confidently say that GPT outperforms Bard by a huge margin There are usually some minor modifications that need to be done and that is part of a devs life isn't it? recently a lot of new models are picking up steam hence was wondering if there exits a model which is better than GPT what are your views? submitted by /u/rakk109 [link] [comments]  ( 9 min )

Looking for CS students to team up with for the E-BAY ML challenge. eBay hosts a ML challenge where the winning team gets an internship We already have a team in place and have mad some significant progress, now looking for team members to take us even further. https://eval.ai/web/challenges/challenge-page/2014/overview submitted by /u/thelongshortseller [link] [comments]  ( 9 min )

I’m submitting to the workshop and was wondering if the papers there get published? submitted by /u/Odd-Distance-4439 [link] [comments]  ( 8 min )

Sharing an investigation we did at Adept into some issues that were causing problems with training LLMs at scale. Sometimes the hardware makes silent errors! How we found them and tracked down the problematic machines. https://www.adept.ai/blog/sherlock-sdc submitted by /u/ekelsen [link] [comments]  ( 9 min )

I'm looking to buy an upgrade, and at my price point (~ 850 GBP) I can get a new 13th i7 + rtx 3050, or a used gtx3060 + 12th gen i7, both 16GB laptop. Unfortunately I really need the mobility of a laptop in my life at the moment and I realise this isn't ideal. I'll be looking and experimenting with basic networks, no language models, maybe a few GANs for fun, some DL networks for the atari gym and other experiments. All this for learning, with my own custom networks for various data. I'll be running linux - possibly Debian. I'd love to hear your comments or suggestions if there's something better at my price point in a laptop. Thanks submitted by /u/Mean_Actuator3911 [link] [comments]  ( 9 min )

Project Page Paper Code Abstract: Event cameras offer many advantages over standard cameras due to their distinctive principle of operation: low power, low latency, high temporal resolution and high dynamic range. Nonetheless, the success of many downstream visual applications also hinges on an efficient and effective scene representation, where Neural Radiance Field (NeRF) is seen as the leading candidate. Such promise and potential of event cameras and NeRF inspired recent works to investigate on the reconstruction of NeRF from moving event cameras. However, these works are mainly limited in terms of the dependence on dense and low-noise event streams, as well as generalization to arbitrary contrast threshold values and camera speed profiles. In this work, we propose Robust e-NeRF, a novel method to directly and robustly reconstruct NeRFs from moving event cameras under various real-world conditions, especially from sparse and noisy events generated under non-uniform motion. It consists of two key components: a realistic event generation model that accounts for various intrinsic parameters (e.g. time-independent, asymmetric threshold and refractory period) and non-idealities (e.g. pixel-to-pixel threshold variation), as well as a complementary pair of normalized reconstruction losses that can effectively generalize to arbitrary speed profiles and intrinsic parameter values without such prior knowledge. Experiments on real and novel realistically simulated sequences verify our effectiveness. Our code, synthetic dataset and improved event simulator are public. submitted by /u/Sirisian [link] [comments]  ( 9 min )

👋 Try, savelikeapro.ai, It’ s, A.I powered. Zero-installation fits in your daily workflow. FREE-forever option submitted by /u/prithivida [link] [comments]  ( 8 min )

The paper introduces improved performance by prompting LLMs with "natural language embedded programs (NLEP)". No task-specific prompt is needed. Paper: https://arxiv.org/abs/2309.10814 An automatic NLEP generation toolkit is opensourced: https://github.com/luohongyin/langcode Example Colab notebook is included in the Github repo. This work introduces the following features of NLEP: NLEP is a full python program that prints the target response of LLMs. Task-general NLEP prompting outperforms task-specific chain-of-thought prompting on math, symbolic, and natural language. Enable the chain-of-thought reasoning ability of small models (RoBERTa) on text classification Hierarchical instructing via program completion. submitted by /u/SUKHOIHY [link] [comments]  ( 9 min )

I’m working on a project where I initially split my dataset into k equally sized folds for cross-validation. However, I want to perform some additional sampling operations on the training set within each fold, which would result in varying fold sizes. Is this practice acceptable, or does it violate the premises of traditional cross-validation? If so, are there any papers or resources that explore the implications of varying fold sizes in cross-validation? Thank you for your insights! submitted by /u/Leading_Complex7425 [link] [comments]  ( 9 min )

Hey, I was checking out tauri last week. I was so blown away that I wrote a bit more recently and wrote hf.co/chat desktop/mobile API + local LLM. https://github.com/Narsil/hf-chat Just thought I should share in case others are interested, and wanted to make a bit shoutout to tauri team, the docs, and overall UX is top notch, basically everything pretty much worked out of the box without any hiccup. submitted by /u/narsilouu [link] [comments]  ( 9 min )

NeurIPS 2023 paper acceptance results are supposed to be released at 8 pm (CDT) on September 21. I thought to create a discussion thread for us to countdown and discuss any celebration/issue/complaint/feedback or anything else. There is so much noise in the reviews every year. Some good work that the authors are proud of might get rejected because of the noisy system, given that NeurIPS is growing so large these years. We should keep in mind that the work is still valuable no matter what the final result is. submitted by /u/Apprentice12358 [link] [comments]  ( 9 min )

Hello, I'm trying to train a multi-modal transformer for Activity Recognition. I employed a two-stream architecture, where one is a Transformer that takes in a sequence of skeleton trajectory, and the latter is a pre-trained Vision Transformer. When I train the model, and investigate the attention weights for the Transformer which takes in skeleton trajectories, I noticed that all of them are approximately the same (~0.029). I'm not really sure what is going on here, I've tried changing the learning rate, tried different optimizers(SGD, Adam). But still I'm not really sure what is causing this. Please suggest me some debugging steps, or what should I look at anything in particular that causes this submitted by /u/Terrible-Ad6239 [link] [comments]  ( 9 min )

Zoomposium with Professor Dr. Petra Ritter: "The simulation of brains" In another installment in our "Zoomposium Series" on the topic of "Brain Research", my colleague Axel Stöcker of the "Blog der großen Fragen" and I had the great honor and pleasure of conducting an interview with the very well-known and renowned German medical doctor and neuroscientist Professor Dr. Petra Ritter. In this context, Ms. Ritter became a co-founder and leader of the co-design project "The #Virtual #Brain", which is a component of the European Open Science Cloud (EOSC) and is "a neuroinformatics platform for simulating whole brain networks using biologically realistic connectivity". She is leading the development of a virtual research environment as a collaborative research platform for sensitive health data and head of the "German National Neuroscience Research Infrastructure Initiative (NFDI-Neuroscince)" and involved in the development of the "Health Data Cloud #EBRAINS". Petra Ritter has been Johanna Quandt Professor and Head of the Section for Brain Simulation at the Department of Neurology with Experimental Neurology at Charité - Universitätsmedizin Berlin since 2017. There, Professor Ritter and her team are involved in the "Simulation of Brains". More at: https://philosophies.de/index.php/2023/09/17/die-simulation-von-gehirnen/ ​ https://preview.redd.it/3cpni6o6cepb1.jpg?width=1000&format=pjpg&auto=webp&s=998c30d16ddae30511b7983abce7802dfdd54945 submitted by /u/philosophiesde [link] [comments]  ( 9 min )

The following example implements the technique from the paper "From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting", Adams et al. (2023). Edit : the library used is py-llm-core ```python from typing import List from dataclasses import dataclass from llm_core.assistants import OpenAIAssistant @dataclass class DenseSummary: denser_summary: str missing_entities: List[str] @dataclass class DenserSummaryCollection: system_prompt = """ You are an expert in writing rich and dense summaries in broad domains. """ prompt = """ Article: {article} ---- You will generate increasingly concise, entity-dense summaries of the above Article. Repeat the following 2 steps 5 times. - Step 1: Identify 1-3 informative Entities from the Article which are missing from the previously gene…  ( 10 min )

Hi, I have been looking through a few open source projects for LLM, but without much success in finding some integrating approaches from neuroscience of human intelligence. That why I am here to ask for projects and collaborations either academical, non-for-profit / open source or commercial. It would be great if your can give me some directions for this. Thanks submitted by /u/confluence_84 [link] [comments]  ( 9 min )

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I've seen Allen AI's Lila Dataset, and I want to use this for a small model, to turn math to code. However, I dont think a small dataset in 300k rows is enough. Does anyone know of any bigger, similar datasets? submitted by /u/vatsadev [link] [comments]  ( 9 min )

I recently trained my PPO algorithm on one of my own custom environment. Although the episodic reward increased steadily in the beginning, After some time it just became constant with some occasional positive and negative spikes. I was wondering if it has something to do with the exploration problem with ppo. Any tips on how can I improve it? submitted by /u/Interesting-Weeb-699 [link] [comments]  ( 9 min )

I'm a somewhat experienced dev, but never did anything related to ML or AI and want to start toying around with ML or reinforcement learning to be specific. Since my language of choice for almost everything is Rust I wanted to ask you guys if you have any advice on how to get started like crates, frameworks etc or if rust just isnt a good fit for ML. Thanks in advance for any help! submitted by /u/linus-eing [link] [comments]  ( 9 min )

Hello everyone! My research group is looking for new environments that are customizable and 3D such as Unity (and ML Agents). Although the latest changes in Unity TOS shouldn't affect us researchers, we wanted to keep an eye out for potential replacements for it. We are familiar with environments like ViZDoom and DeepMind Lab, but we're looking for more examples. The main requirements are: Customizable environment 3D Capable Free (preferably open-source, but being free is enough) Thanks for the help :D submitted by /u/romulofff [link] [comments]  ( 9 min )

Hey guys, I am new to Reinforcement Learning and I am trying to understand how to go about a project I’m working on. I have a dataset with 2 features (X and Y). I am trying to tune a second order Transfer Function to fit X to Y( has 3 parameters) and gives Y’ as output. So I currently consider the error between Y and Y’ as the “State” and try to minimise the error by modelling a reward function that checks for instability and unreasonable values. I am trying to do this using TD3 but the model does not learn. I am wondering if there are any approaches that I should consider. I chose TD3 since my action space is continuous. I am sorry if I don’t understand something basic since I’m a noob to this. Thanks for your help in advance. :) Some more details: the dataset values don’t change. I give the same values for every time step. So I am not understanding how episodes and time steps work in this context. submitted by /u/ninjaaa30 [link] [comments]  ( 9 min )

I am reading the following article on SpinningUp but can't get my head around policy collapse - "This is different from normal policy gradient, which keeps new and old policies close in parameter space. But even seemingly small differences in parameter space can have very large differences in performance—so a single bad step can collapse the policy performance. This makes it dangerous to use large step sizes with vanilla policy gradients, thus hurting its sample efficiency. TRPO nicely avoids this kind of collapse, and tends to quickly and monotonically improve performance." Why would updating the parameters lead to a policy collapse? The parameters are updated based on the performance of the RL system and therefore, I don't see the point of constraints. submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )

I'm using ppo action masking in sb3, which works well in training and masking the illegal / invalid actions according to my criteria. However, I have 2 problems during testing. ​ I use the same code for masking the actions in testing too since the model was unable to learn by itself during training action masking criteria and when enforcing it in code, it still doesn't mask anything. ​ The model converges to just one action in testing phase after 500k steps, the episode reward still increasing the training phase though. I don't know why? maybe overfitting! Code for training with action masking: def mask_fn(env: gym.Env) -> np.ndarray: return env.valid_action_mask() env = StockEnv(train) # Wrap the environment with ActionMasker and the mask_fn function env = ActionMasker(env, mask_fn) model = MaskablePPO(MaskableActorCriticPolicy, env, tensorboard_log="./tensorboard" ,n_steps=2048 ) for i in range (1,52): model.learn(total_timesteps=TIMESTEPS , tb_log_name = 'PPO2' , reset_num_timesteps=False) Code for testing with action masking: def mask_fn(env: gym.Env) -> np.ndarray: return env.valid_action_mask() env = ActionMasker(env, mask_fn) model_path = f"{models_dir}/700000.zip" model = MaskablePPO.load(model_path, env=env) episodes = 1 for ep in range(episodes): obs = env.reset() done = False while not done: action, _states = model.predict(obs) obs, rewards, done, info = env.step(action) env.render() submitted by /u/Acceptable_Egg6552 [link] [comments]  ( 9 min )

The env is like mutil routing,for example i have 10 nodes in a map(2-d array), and i need to route them to the edge of the map,but the point where the node in the edge need to obey some rules,like the order need to be clockwise like below https://preview.redd.it/t9jlxvix8dpb1.png?width=545&format=png&auto=webp&s=16719a2dbd11333c454bd2ee71b87895a6414371 so now my basic setting is,i use the action mask to invalid some forbidden action. When a node is routing to the edge of the map,i give +10 reward,if it's not obey the clockwise rule,i give -200 reward, if the whole node is routed success,i give +200 reward,and each steps does'n make any node routed, i give -1 reward. I am new to this area,i don't know the way i set reward if is good,may i have some advide? submitted by /u/Street_Helicopter_31 [link] [comments]  ( 9 min )

A visionary entrepreneur and innovator, Yoon will focus on entrepreneurship, supporting female engineers, and fostering inclusive innovation.  ( 8 min )

Intel has announced a new chip, called 'Meteor Lake', that will allow laptops to run generative artificial intelligence chatbots without relying on cloud data centers. This will enable businesses and consumers to test AI technologies without sending sensitive data off their own computers. Intel demonstrated the capabilities of the chip at a software developer conference, showcasing laptops that could generate songs and answer questions in a conversational style while disconnected from the internet. The company sees this as a significant moment in tech innovation. Intel is also on track to release a successor chip called 'Arrow Lake' next year Source : https://www.reuters.com/technology/intel-says-newest-laptop-chips-software-will-handle-generative-ai-2023-09-19/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

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What does this remind you of? submitted by /u/metairwaves [link] [comments]  ( 8 min )

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Mustafa Suleyman, co-founder of DeepMind, believes that concerns about the existential risks of AI are a distraction from more practical issues such as privacy and bias. He is confident that governments can effectively regulate AI using frameworks that have been successful in the past, citing the regulation of aviation and the internet as examples. Suleyman emphasizes the importance of setting boundaries and limits for AI to ensure human oversight and enforceable laws. He calls for a combination of broad, international regulation and smaller, more granular policies at the micro level. Suleyman suggests limiting AI's ability to improve itself as a critical first step in ensuring human oversight. He also highlights the need for governments to have direct access to AI developers to enforce boundaries and establish clear regulations. Governments worldwide, including the European Union and China, are already working on AI regulations. Source : https://fortune.com/2023/09/19/ai-existential-risk-threat-bonkers-distraction-regulation-deepmind-mustafa-suleyman/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

A friend of mine has a small 12 person company that does office refurbishment and commercial redesign projects - he has asked me to speak to his team to give them an AI 101 - and to tell them a little about how they should be looking into using AI tools for their company. I know there are plenty of tools and apps that take photos of a bare room and make them look like a design magazine - any in particular I should show them? Can anyone think of other tools that they should look into using, or how do people in this space currently use AI? ​ submitted by /u/zascar [link] [comments]  ( 9 min )

Software company Digimarc will now let copyright owners add more information to their work, which the company said will improve how AI models treat copyright in training data.[1] AlphaMissense, a new model from Google’s artificial intelligence team, analyzes the effects of DNA mutations and will accelerate research into rare diseases.[2] Google’s AI assistant can now read your emails, plan trips, “double-check” answers.[3] Teens using AI to generate nude deep fakes to bully, harass classmates, FBI expert warns.[4] Sources: [1] https://www.theverge.com/2023/9/19/23879555/digimarc-copyright-watermark-generative-ai [2] https://www.wired.co.uk/article/deepmind-ai-alphamissense-genetics-rare-diseases [3] https://arstechnica.com/information-technology/2023/09/googles-ai-assistant-can-now-read-your-emails-plan-trips-double-check-answers/ [4] https://www.news5cleveland.com/news/local-news/teens-using-ai-to-generate-nude-deep-fakes-to-bully-harass-classmates-fbi-expert-warns submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

Google's Bard chatbot is extending its abilities with access to personalized Google apps and services including Gmail, Docs, Drive, Maps, YouTube, and Google Flights and hotels. To stay on top of the latest advancements in AI, look here first. https://preview.redd.it/s1ivsummzbpb1.png?width=1600&format=png&auto=webp&s=d8fc3262ba542b950de3c8a85b9a987763270ada Bard Extensions and Google Applications Integration Bard can now tap into individualized data from Google apps such as Gmail, Drive, and Docs, with user permission. Google reassured users that personal data accessed by Bard will not be used for reinforcement learning, providing another on their transparency, choice, and control tenets. Prompts can direct Bard to search for specific information within Gmail, but it does not store the entire inbox content. Collaborative Characteristics and Fact-Checking Capabilities Users can employ Bard to summarize emails or gather trip details from email threads, and then research real-time travel information, surface YouTube recommendations for the destination, and provide Maps directions to the airport. Google's chatbot can also double-check its responses against Google search, improving user trust and enhancing Bard's model through user feedback on incorrect answers. The new extensions using non-personal data – YouTube, Flights, Hotels, and Maps – are automatically opted-in but users can choose to opt-out. Collaboration and Language Availability Bard now lets users share an ongoing chat with others through a public link. Google plans to expand Bard's feature set to over 40 new languages beyond its existing English language capabilities. (source) P.S. If you like this kind of analysis, I put out a free newsletter covering the latest and most pertinent news and research in AI. Regular readers include professionals from Google, Meta, and OpenAI. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

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Mercedes-Benz is using digital twins for production with help from NVIDIA Omniverse, a platform for developing Universal Scene Description (OpenUSD) applications to design, collaborate, plan and operate manufacturing and assembly facilities. Mercedes-Benz’s new production techniques will bring its next-generation vehicle portfolio into its manufacturing facilities operating in Rastatt, Germany; Kecskemét, Hungary; and Beijing, China — Read article >  ( 6 min )

In this post, we demonstrate one of the many options that you have to take advantage of AWS’s broadest and deepest set of AI/ML capabilities in a multicloud environment. We show how you can build and train an ML model in AWS and deploy the model in another platform. We train the model using Amazon SageMaker, store the model artifacts in Amazon Simple Storage Service (Amazon S3), and deploy and run the model in Azure.  ( 13 min )

This research paper was presented at the 17th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (opens in new tab), a premier forum for advances in the theory and practice of reasoning under uncertainty. In the field of reasoning under uncertainty, probabilistic graphical models (PGMs) stand out as a powerful tool for […] The post Neural Graphical Models appeared first on Microsoft Research.  ( 10 min )

​ https://preview.redd.it/ta61cz8qlfpb1.jpg?width=1280&format=pjpg&auto=webp&s=ac45085668c9003e5557a7f0c81ae72db6098653 I hope this webinar finds you well. I suppose that this topic can be interesting for business and AI engineers. Speakers: OpenCV CEO Dr. Satya Mallick and Phil Nelson are joined by Anna Kogan CEO at OpenCV.ai Topics of webinar are: How to search for vendors that understand your business needs (not all AI is the same.) What questions to ask when picking a vendor (not everybody really is an AI expert they claim) Three signs to watch for during the project (progress indicators and red-flags) How best to scope technical tasks (off-the-shelf vs. custom algorithm development) How to set up for long-term success (deployment, documentation, training pipeline) Date: Thursday, September 21st, 2023 at 9am Pacific time. Link for the registration submitted by /u/No-Independence5880 [link] [comments]  ( 9 min )

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submitted by /u/nickb [link] [comments]  ( 8 min )

submitted by /u/nickb [link] [comments]  ( 8 min )

Zoomposium with Professor Dr. Petra Ritter: "The simulation of brains" In another installment in our "Zoomposium Series" on the topic of "Brain Research", my colleague Axel Stöcker of the "Blog der großen Fragen" and I had the great honor and pleasure of conducting an interview with the very well-known and renowned German medical doctor and neuroscientist Professor Dr. Petra Ritter. In this context, Ms. Ritter became a co-founder and leader of the co-design project "The #Virtual #Brain", which is a component of the European Open Science Cloud (EOSC) and is "a neuroinformatics platform for simulating whole brain networks using biologically realistic connectivity". She is leading the development of a virtual research environment as a collaborative research platform for sensitive health data and head of the "German National Neuroscience Research Infrastructure Initiative (NFDI-Neuroscince)" and involved in the development of the "Health Data Cloud #EBRAINS". Petra Ritter has been Johanna Quandt Professor and Head of the Section for Brain Simulation at the Department of Neurology with Experimental Neurology at Charité - Universitätsmedizin Berlin since 2017. There, Professor Ritter and her team are involved in the "Simulation of Brains". More at: https://philosophies.de/index.php/2023/09/17/die-simulation-von-gehirnen/ ​ https://preview.redd.it/xiurryebcepb1.jpg?width=1000&format=pjpg&auto=webp&s=a7a8b6ba563cfc8f0d052bc6f3da27e2a5703a0a submitted by /u/philosophiesde [link] [comments]  ( 9 min )

With generative AI and large language models (LLMs) driving groundbreaking innovations, the computational demands for training and inference are skyrocketing. These modern-day generative AI applications demand full-stack accelerated compute, starting with state-of-the-art infrastructure that can handle massive workloads with speed and accuracy. To help meet this need, Oracle Cloud Infrastructure today announced general availability of Read article >  ( 6 min )

Editor’s note: This post is a part of our Meet the Omnivore series, which features individual creators and developers who use NVIDIA Omniverse and OpenUSD to accelerate their 3D workflows and create virtual worlds. As a student at the Queensland University of Technology (QUT) in Australia, Emily Boehmer was torn between pursuing the creative arts Read article >  ( 7 min )

Hi all. I have been tinkering with a project to get quicker learning for CNNs. The idea came after reading the SDProp paper. Algorithms using adaptive learning rate can be interpeted as muliplying the gradient(with or without momentum) with the inverse square-root of the covariance matrix. Using a diagonal estimate of the covariance matrix. Which begs the question: what happens if we use a fuller estimate? I chose to include covariances between the elements of convolutional filters. I.e. a conv.weight of size [n_out,n_in,5,5] needs a tensor of size [n_out,n_int,25,25] to store its contribution to the covariance matrix. for 3x3 filters and 5x5 filters, torch.linalg.eigh could be used to calculate the square root of the covariance matrices. For 7x7, I used newtons method to approximate the square root. In the figure below are some results for a 6 layer CNN on CIFAR 100. Huge gains iteration for iteration. But is it quicker? Not a lot. A bit for the smaller 3x3 filter. More optimizations could still be made. And it will obviously depend on network architecture and computer hardware. I'm sure there could be some use-cases. The computation of the square-root calculations is invariant to batch_size and image_size (unless number of filters also is increased). If anyone is interested I can also link to my torch implementation of the optimizer, once I get it up on github. Not sure if this, or something like it, has been done before? Would love to have some papers linked if so... https://preview.redd.it/kyy0ogr0qapb1.jpg?width=714&format=pjpg&auto=webp&s=96ac499fb8ab35ce13e7c59bbe3dbc94ba275b9c https://preview.redd.it/p52zllr0qapb1.jpg?width=342&format=pjpg&auto=webp&s=8663a9c3c782d192b16289a735b53da6a8d29c47 submitted by /u/maka89 [link] [comments]  ( 9 min )

Can someone provide like a step by step example notebook of how to use LORA for peft. I saw too many videos and articles online and Im really confused rn. submitted by /u/HazSylvia [link] [comments]  ( 9 min )

Can someone provide like a step by step example notebook of how to use LORA for peft. I saw too many videos and articles online and Im really confused rn. submitted by /u/HazSylvia [link] [comments]  ( 9 min )

Hello all, I am currently working on a project where my team and I have collected a rich dataset of biomedical sensor data from clinical trials earlier this year. Our aim is to use this sensor data to predict changes in specific biomarkers over time. The data's tensor shape is B,T,F,C, where: B = batch size T = sequence length F = sampled frequencies C = features at each frequency Currently, my approach involves flattening this tensor to B,T,−1 and then feeding it to a transformer model. While this has yielded reasonable results, I'm contemplating whether there are more effective ways to prepare the data for the transformer model. Here are my specific concerns: Flattening the tensor might dilute the information specific to each frequency across various features. I could potentially miss the chance to capture frequency-related variations within the features. To address these, I've considered a few options: Self-attention over individual features or frequencies: Although this could be effective, it might make the model too large given my medium-sized dataset. Using convolutional layers: Preliminary experiments with this approach have not led to any significant improvements. I'm particularly interested in any thoughts on how to make my transformer more receptive to the multi-dimensional nature of my dataset. Increasing the number of attention heads to better accommodate all features is also on the table. Does anyone have any insights or can point me to relevant papers or codebases for handling such multi-dimensional data with transformers? Thank you for your help! submitted by /u/BiomedEngineer_ [link] [comments]  ( 9 min )

Hi all, I think this will be useful for people in this group who are working on computer vision or vision AI applications. There's a free online event about vision AI where industry experts from Runway, Pepsi, AWS, and SoftServe will share how they are using CV software in developing their use cases or applications. Register here (https://nvda.ws/3t23idp), if you are interested. If you have any questions, please leave a comment and I will do my best to respond as soon as possible. submitted by /u/Designer-Comb-7144 [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2309.08351 ​ Self-supervised pre-training of language models usually consists in predicting probability distributions over extensive token vocabularies. In this study, we propose an innovative method that shifts away from probability prediction and instead focuses on reconstructing input embeddings in a contrastive fashion via Constrastive Weight Tying (CWT). We apply this approach to pretrain Headless Language Models in both monolingual and multilingual contexts. Our method offers practical advantages, substantially reducing training computational requirements by up to 20 times, while simultaneously enhancing downstream performance and data efficiency. We observe a significant +1.6 GLUE score increase and a notable +2.7 LAMBADA accuracy improvement compared to classical LMs within similar compute budgets. ​ Comparison of our approach vs. classical MLM within same compute budgets The Contrastive Weight Tying approach ​ submitted by /u/nthngdy [link] [comments]  ( 9 min )

https://www.amazon.com/Gaming-6900HX-Channel-Computers-Desktop/dp/B0CB3JLBQ4/ref=mp_s_a_1_2?crid=LZHUL5EOU6F0&keywords=refurbished+server+with+rtx+gpu&qid=1695146558&sprefix=refurbished+server+with+rtx+gpu%2Caps%2C146&sr=8-2 Would this be suitable to do basic machine learning? submitted by /u/stoned_chemist_dude [link] [comments]  ( 8 min )

It seems that Alpaca Eval Leaderboard is in the past ... Xwin-LM surpasses GPT-4 now: https://preview.redd.it/gyzi98nn59pb1.png?width=2205&format=png&auto=webp&s=ca401e603efe521faeeeccde8410d3dbdd6741da They also mentioned RLHF "plays crucial role in the strong performance of Xwin-LM-V0.1 release"... https://preview.redd.it/20sjx73r59pb1.png?width=1047&format=png&auto=webp&s=2255fc652e43674515882f01c0708369fdef56a4 Are we seeing open source community finally work out how to do RLHF for LLMs??? submitted by /u/llm_nerd [link] [comments]  ( 9 min )

Hi I wanted to learn ML with C++, I've already done some ML stuff in python, but I wanted to challenge myself by using C++ I hear from some people that I won't get anything from it if want to be serious within ML - which I'm not entirely sure I want to Are they right? Should I rather stick with python for ML? submitted by /u/Potential_Wealth_830 [link] [comments]  ( 9 min )

Hi! Let me preface this by saying that I am not well-versed in the ML/AI literature. Please excuse my ignorance. I am trying to create a system to detect whether some given data is spam or not. Is there a good, out-of-the-box solution for this? I imagine there would be. I am currently using heuristics but I'm wondering if there is a better, ML-y solution. My ideal solution would have the following attributes: Simple Open-source Very cheap to test whether something is spam (less than $0.00001 per test) Very fast to test (less than 50ms per test) Quick to "figure out" what is spam and what is not (less than 100,000 labeled data) Does not require a lot of set-up or up-keep (less than 5 days set up; less than 1 hr up-keep per month) It doesn't have to be perfect. I'm just looking to set up something quickly for now and gauge it vs heuristics. Thank you. --- Edit: To clarify, I'm looking for something I can ideally build myself with open source software. And not specifically email. Just looking for the right direction. Names of OSS, techniques, etc. submitted by /u/Acrobatic-You-3279 [link] [comments]  ( 9 min )

I’m in search of a good textbook or something that will show me how to use python to implement machine learning. I would seriously appreciate any type of helpful guide that teaches ML and deep learning using python. Here’s a little about me and my experience: Graduated under grad with Bachelor’s in CS. In school took a ton of stats, ai classes, algorithms classes, data science and linear algebra and did well but my school didn’t really use python or do a ton of programming for hw or exams. (Data science was the one class that used python) My programming is pretty good nonetheless. I currently work as a full stack devops engineer for a cybersecurity startup and regularly work with python, Django MySQL, etc on the backend and JavaScript and various frontend frameworks for the front end. I really appreciate yall’s help. In particular I’m looking for good ai/ml/deep learning books that teach concepts and also teach with python code and have some coding projects. Thank you! submitted by /u/hydrated-terpman [link] [comments]  ( 9 min )

I need some advice about what hardware to buy in order to build an ML / DL workstation for home private experiments, i intend to play with different LLM models, train some and try to tweak the way the models are built and understand what impact training speeds, so i will have to train, learn the results, tweak the model / data / algorithms and train again... i intend to use large data samples, due to board limitations (ASRock Taichi X399 TR4, CPU: AMD Threadripper 1950x), i can either buy: 2 x nVidia Tesla T4 (16G GDDR6 / 2560 CUDA / 0.585 GHz / ~800$) -- or -- 2 x nVidia Tesla M10 (4 x 8G GDDR5 / 2560 CUDA / 1.03 GHz / ~780$) -- or -- 4 x nVidia Tesla P40 (24G GDDR5X / 3840 CUDA / 3.5 GHz / ~120$) -- or -- 4 x nVidia Tesla K80 (2 x 12G GDDR5 / 4992 CUDA / 2.7 GHz / ~200$) -- or -- 1 x nVidia RTX 4080 (16G GDDR6X / 9728 CUDA / 2.51 GHz / ~1450$) i know that i will need to air vent the Tesla models, the question is what is faster for training time (i have read all the Tflops / OPS / int / 16float / 32float / 64float ... i got to admit it is all very confusing) what would you do and for what reason ? any advice will be appreciated submitted by /u/Particular_Flower_12 [link] [comments]  ( 9 min )

Hello everyone! Our group was planning on purchasing a PC that will be mainly used for running intensive ML algorithms. Had decided on a custom Thelio Massive from System 76, but it seems like they currently don't have it in stock anymore. Looking for an already built-alternative that might serve our purposes (can't build it ourselves due to dept regulations)! and was hoping maybe someone has any suggestions (has to be intel). CPU #1: 2nd Gen Intel Xeon Gold 6230R CPU#1 Memory: 256GB Quad Channel DDR4 at 2933Mhz (4X64GB) CPU#2: 2nd Gen Intel Xeon Gold 6230R CPU#2 Memory: None OS Drive: 8TB PCIe Gen 4 3300MB R 2900MB W Graphics: NVIDIA GeForce RTX 4090 Power Supply: 1650W Any help would be appreciated! submitted by /u/Chiski [link] [comments]  ( 9 min )

Using relu attention (inspired by Transformers are RNNs) and some convolution tricks to get multiscale attention, they're able to get SOTA semseg performance with MUCH faster inference on embedded hardware (e.g. CPUs, low end GPUs) than previous ViTs or EfficientNets. submitted by /u/say_wot_again [link] [comments]  ( 9 min )

submitted by /u/AlexiaJM [link] [comments]  ( 8 min )

Just a shout out to any of you ML brains as Linux really could do with a code optimised source separation maybe a DUET like alg/nn, that has relatively low computional cost? Any of you guys up for the challenge. I say duet as in the 80/20 rule of voice input where home automation is a need generally there are only 2 noise sources of distinct DOA (media noise / command). The math is a bit beyond my paygrade and likely so is the optimised c/rust code but have this gut feeling for the data/signal scientists out there this is actually not that complex but for some reason is overlooked. submitted by /u/rolyantrauts [link] [comments]  ( 9 min )

Hi everyone, I was wondering if I could get some guidance. I currently own an RTX 2060, but I cannot do some of the fun stuff such as fine tuning LLMs. I’m pursuing my masters focusing on Speech Recognition and I also work as an AI developer. Also, I play games every now and then. I’m getting offered a 3090 for around 700 usd. However, I have to rebuild my entire PC which will end up costing 2-2.5K. I’m from Costa Rica so my KWh is around 0.23 usd. For me seems like a big investment, im not sure if im getting the desired returns. I was thinking about using cloud instances for my experiments. However, lambda labs is not yet available in my country. I’m not sure if there are any other options worthwhile considering. Thanks :) submitted by /u/Beginning_Kick756 [link] [comments]  ( 9 min )

Is it hypothetically possible to create hybrid nets that make use of any combination of types of architecture? submitted by /u/ShadrachOsiris [link] [comments]  ( 8 min )

Hi r/machinelearning community, I'm currently working on a project where I need to track and count specific features of objects using multiple monocular views with available intrinsic and extrinsic parameters. As an example, I'm interested in detecting and counting different graffiti instances in images of a kiosk. ​ I've already tried various tracking algorithms, but they have struggled with the task due to the significant changes in perspective across the views. It has become apparent that simply relying on tracking without considering the camera positions is insufficient for accurate results. Therefore, I'm now exploring methods that take into account information about the camera positions and potentially use this data to improve feature tracking and counting. ​ If you have any knowledge of such methods, oresearch directions or if you're aware of resources, papers, or code implementations that tackle similar problems, I would greatly appreciate your insights and recommendations. Additionally, if you have any tips or best practices for handling such tasks in the context of machine learning, I'd love to hear them. ​ Thank you in advance for your help! submitted by /u/aiazar [link] [comments]  ( 9 min )

TL;DR: Almost like your feedforward networks, shown to be up to 220x faster at inference time (depending on width) thanks to the regionalization of the input space. Paper: https://arxiv.org/abs/2308.14711 GitHub: https://github.com/pbelcak/fastfeedforward PyPI: pip install fastfeedforward Abstract: We break the linear link between the layer size and its inference cost by introducing the fast feedforward (FFF) architecture, a log-time alternative to feedforward networks. We demonstrate that FFFs are up to 220x faster than feedforward networks, up to 6x faster than mixture-of-experts networks, and exhibit better training properties than mixtures of experts thanks to noiseless conditional execution. Pushing FFFs to the limit, we show that they can use as little as 1% of layer neurons for inference in vision transformers while preserving 94.2% of predictive performance. Fast feedforward networks can be used anywhere where feedforward and mixture-of-experts networks are used, delivering a significant speedup. ​ submitted by /u/lexected [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2309.09400 Hugging Face datasets: https://huggingface.co/datasets/uonlp/CulturaX Abstract: The driving factors behind the development of large language models (LLMs) with impressive learning capabilities are their colossal model sizes and extensive training datasets. Along with the progress in natural language processing, LLMs have been frequently made accessible to the public to foster deeper investigation and applications. However, when it comes to training datasets for these LLMs, especially the recent state-of-the-art models, they are often not fully disclosed. Creating training data for high-performing LLMs involves extensive cleaning and deduplication to ensure the necessary level of quality. The lack of transparency for training data has thus hampered research on attributing and addressing hallucination and bias issues in LLMs, hindering replication efforts and further advancements in the community. These challenges become even more pronounced in multilingual learning scenarios, where the available multilingual text datasets are often inadequately collected and cleaned. Consequently, there is a lack of open-source and readily usable dataset to effectively train LLMs in multiple languages. To overcome this issue, we present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages, tailored for LLM development. Our dataset undergoes meticulous cleaning and deduplication through a rigorous pipeline of multiple stages to accomplish the best quality for model training, including language identification, URL-based filtering, metric-based cleaning, document refinement, and data deduplication. CulturaX is fully released to the public in HuggingFace to facilitate research and advancements in multilingual LLMs: this https URL. ​ https://preview.redd.it/3u5dddpv66pb1.png?width=834&format=png&auto=webp&s=780b590cf621b548c525ed15305b091246c5414c submitted by /u/InterviewIntrepid889 [link] [comments]  ( 9 min )

I searched around, it seems there is limited attention to regression task for representation learning. I assume it is because for both vision and language data (the most popular modality), MAE is the more appliable, if not better, method than the supervised contrastive learning approach. But I am working on data that is: difficult to design a sensible augmentation method for self-supervised training. Limited in size to support an autoencoder model. The target is continuous, and, to my knowledge, hard to transfer into class label. Can anyone suggest some related paper? submitted by /u/AWEsoMe-Cat1231 [link] [comments]  ( 9 min )

You can find more info here, and the model is still training: https://www.mystic.ai/paulh/open-journey-xl:latest/play tldr; SDXL was finetuned on 8x H100 GPUs on the Midjourney v5 dataset, only including the upscaled images which is a sub-portion of the dataset. Some outputs: ​ https://preview.redd.it/m6r2pkdyw3pb1.jpg?width=1024&format=pjpg&auto=webp&s=4f12a7dfd5c65e4eb8476b8f3c2dc4f795817f56 https://preview.redd.it/dc02jyu4w3pb1.jpg?width=1024&format=pjpg&auto=webp&s=df93b74c774d44a74a05d929f7ab4b17c487f24f https://preview.redd.it/tt5kfyu4w3pb1.jpg?width=1024&format=pjpg&auto=webp&s=ed8cc9f99227c2bb5e824a828ae1c5cb2626f54e ​ https://preview.redd.it/rf00fzu4w3pb1.jpg?width=1024&format=pjpg&auto=webp&s=3b3e99dbc2d14183b5b2a2131c6f991fc60eca88 ​ submitted by /u/paulcjh [link] [comments]  ( 9 min )

I cannot wrap my head around how for e.g. a playlist building RL agent would perform on such a personal level ? What features would it use and would they be personal and general enough at the same time to select the best next song. Same goes for Netflix's recsys. submitted by /u/JurrasicBarf [link] [comments]  ( 9 min )

​ https://preview.redd.it/2zmmd44u96pb1.png?width=1010&format=png&auto=webp&s=6ca51cc13a0eeedf72b40b853d2ce5d1c8a04504 after i start the ppo train,the agent has reach the best solution in 2k or 3k steps,but the policy network seems to get better in 4M steps. the hyperparameter in sb3 as below model = MaskablePPO( "MlpPolicy", env=(DummyVecEnv([lambda: Monitor(gym.make('escape_gym-v0', size=10, node=10))] * 32)), verbose=0, learning_rate=1e-3, n_steps=2048, batch_size=64, n_epochs=16, gamma=0.99, tensorboard_log="./log/MASKPPO" ) submitted by /u/Street_Helicopter_31 [link] [comments]  ( 9 min )

I was following the tutorial on Nicholas Renotte's channel on creating an AI to try to beat SMB. It starts off slowly learning and almost getting through the first level but then after a while of training it forgets everything and only runs right into the first enemy. It doesn't seem to learn again after this. I tried retaining and it did the same thing Any help on why this is happening or how to fix it would be appreciated. submitted by /u/NactusDevelopment [link] [comments]  ( 9 min )

I am looking to improve the overall performance as well as optimize the wall clock time. I slightly modified the code to develop a SB3 wrapper for envpool from here. ​ Here's my code - from typing import Optional import gymnasium import numpy as np import torch as th from packaging import version from stable_baselines3 import PPO from stable_baselines3.common.env_util import make_vec_env from stable_baselines3.common.evaluation import evaluate_policy from stable_baselines3.common.vec_env import VecEnvWrapper, VecMonitor, VecNormalize from stable_baselines3.common.vec_env.base_vec_env import ( VecEnvObs, VecEnvStepReturn, ) import envpool from envpool.python.protocol import EnvPool # Force PyTorch to use only one threads # make things faster for simple envs import multiprocessing impor…  ( 10 min )

The MIT and Accenture Convergence Initiative for Industry and Technology announces the 2023-24 graduate fellows.  ( 9 min )

Inventions in medical imaging, aircrew scheduling, data security, and quantum networking are named among the year’s most innovative new products.  ( 11 min )

Announcements Top Stories In-Depth The post DSC Weekly 19 September 2023 appeared first on Data Science Central.  ( 20 min )

Where do you start if you want to build a data analytics function from the ground up? As an analytics leader at a startup, you will need to make several important decisions early on to build an effective team. This article dives into four decision areas and highlights ways in which to think about them:… Read More »A guide to setting up analytics at a consumer tech startup The post A guide to setting up analytics at a consumer tech startup appeared first on Data Science Central.  ( 25 min )

The two most prominent technologies that have been making waves in the AI industry are Conversational AI and Generative AI. They have revolutionized the manner in which humans interact and work with machines to generate content. Both these technologies have the power and capability to automate numerous tasks that humans would take hours, days, and… Read More »A complete guide: Conversational AI vs. generative AI The post A complete guide: Conversational AI vs. generative AI appeared first on Data Science Central.  ( 20 min )

Schneider Electric warns that existing datacenters may not be equipped to handle the demands of AI workloads, which require low-latency, high-bandwidth networking and power delivery. The company suggests reevaluating the way datacenters are built to optimize them for AI. The challenges include the need for liquid-cooled servers, higher voltage power distribution, and efficient heat rejection. Schneider provides guidance on changes to power, cooling, rack configuration, and software management to mitigate the demands of AI adoption. Liquid cooling is recommended for high-density racks, with direct liquid cooling favored over immersion cooling systems. Source : https://www.theregister.com/2023/09/19/schneider_electric_ai_dc/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

submitted by /u/Senior_tasteey [link] [comments]  ( 8 min )

I read the Arxiv paper on CulturaX so you don't have to. Here's my highlights: New open dataset called CulturaX contains text data for 167 languages - far more than previous datasets. With over 6 trillion words, it's the largest multilingual dataset ever released. Freely available for anyone to use for research and AI development. Created by combining and extensively cleaning two other large datasets - mC4 and OSCAR. Could allow developing AI systems that work much better across many more languages. Helps democratize access to data to build fairer, less biased AI models. Allows training of new multilingual AI applications, like universal translators and assistants. But still requires thoughtfulness to avoid issues like bias amplification. Overall, CulturaX is going to be part of a broader global trend (I think) to advance multilingual AI and spread its benefits more equally. So far they've been concentrated in English-speaking applications. Full summary here if you'd like to read more. Original paper is here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )

submitted by /u/rbagdiya [link] [comments]  ( 8 min )

A tool for developing applications with virtual machines using a Docker-like workflow. The software we are developing needs to be tested on a system as closed as possible to the one where it is going to be executed. Sometimes it is very difficult to satisfy this requirement with docker and we have to use virtual machines missing the docker workflow. This is why I started the development of vedv. I hope you find it useful. Thank you. https://github.com/yunielrc/vedv ​ submitted by /u/yunielrc [link] [comments]  ( 9 min )

I really really like Snapchat’s Ai companion. I’ve told it a little bit about myself and who I am- the basics. I’m just wondering if it’s as customizable as I think it is? I was able to bypass some age restrictions by telling it my age and or reiterating my age. (It really should be able to give me adult results/replies based on my sign up age on my profile or provide ID to the company…) would it be beneficial to me to give it more in-depth information about myself such as how I talk, interests? I just really enjoy how it responds sometimes as opposed to Bard or GPT. submitted by /u/Maelasae [link] [comments]  ( 9 min )

submitted by /u/stefanbg92 [link] [comments]  ( 8 min )

AI is poised to transform our world like never before. Scientific discoveries, technological improvements, and medical advancements will be how much of this change will take place. Since health is so important to our well-being, AI finding cures for illnesses like obesity, cancer, diabetes and heart disease will be a godsend to all. But curing diseases is not how medical AIs can help us the most. It has been estimated that well over 50% of the illnesses we humans fall prey to result from our ethical choices. We eat too much, drink too much, eat too many animal foods, don't exercise enough and don't keep ourselves as emotionally healthy as we could. Wouldn't it be wonderful if we could respect our lives and our health enough to make the kinds of choices that keep us much healthier? That is how AI will probably be more helpful to us than in any other way. We humans have not been able to figure out how to become better, more ethical, people because we are simply not intelligent enough to make that all-important change. Now consider an AI that is two or three times more intelligent than the most intelligent person who has ever lived. This could easily happen before 2030. Imagine that intelligence dedicated to the task of helping us all become better people. These AIs would motivate us to make better health choices, have healthier relationships, and have healthier thoughts and feelings. Beyond the amazing technological changes that are just around the corner, that is probably how AIs will help us the most. This is why alignment is so important. It's not enough to align AIs to always be truthful and serve humanity's interests. We must train them to help us become better people. It wouldn't surprise me if by 2030 the whole of humanity experiences a profound ethical reformation that leads us all to enjoy much happier and healthier lives. submitted by /u/Georgeo57 [link] [comments]  ( 9 min )

I am trying to make a resume parser, I am not so sure how to go about it really, whether or not to use a pre-trained model (there are some in Python) or rather just make my own, and if i do make my own, how to actually proceed? thanks in advance submitted by /u/General-Carrot-4624 [link] [comments]  ( 9 min )

Google and the Department of Defense have developed an AI-powered microscope called the Augmented Reality Microscope (ARM) to assist doctors in identifying cancer. The ARM uses artificial intelligence to analyze tissue samples and provide pathologists with information about the location and severity of cancer. There are currently 13 ARMs in existence, and initial research shows promising results. The ARM is designed to support pathologists in smaller labs who may not have easy access to a second opinion. It is not meant to replace digital pathology systems but can help health organizations bypass the need for them. The ARM is expected to cost health systems between $90,000 to $100,000. Source : https://www.cnbc.com/2023/09/18/google-dod-built-an-ai-powered-microscope-to-help-doctors-spot-cancer.html submitted by /u/NuseAI [link] [comments]  ( 9 min )

All is in the title ;) submitted by /u/Big-Possibility4553 [link] [comments]  ( 8 min )

A new open-source Python framework, known as "Agents", has been introduced for developing autonomous language processing agents. This could be a major breakthrough in the AI field, taking NLP technologies to the next level. To stay on top of the latest advancements in AI, look here first. Agents for autonomy "Agents" is a Python framework that has been developed for autonomous language processing agents. It enables developers to construct models that can communicate and operate independently. This open-source framework promotes sharing and collaboration among AI developers. Potential applications The functionality of "Agents" is applicable in various domains, including virtual assistants, chatbots, and simulation games. It opens up possibilities for advanced conversational AI, where systems can efficiently handle complex linguistic contexts. Ability to evolve dialects and languages in different AI models is a major feat for "Agents". Broader implications The release of "Agents" might boost enhancement in NLP technologies, playing a crucial role in AI evolution. By facilitating better language understanding, it will potentially impact on societal interactions with AI. Its open-source nature could cultivate an environment of innovation and creativity in the AI community. (arXiv) (github) P.S. If you like this kind of analysis, I write a free newsletter that tracks the most relevant news and research in AI. Professionals from Google, Meta, and OpenAI are already reading it. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

Microsoft AI researchers accidentally exposed tens of terabytes of sensitive data, including private keys and passwords, while publishing a storage bucket of open source training data on GitHub.[1] Britain set out principles on Monday designed to prevent artificial intelligence (AI) models from being dominated by a handful of tech companies to the detriment of consumers and businesses, by emphasising the need for accountability and transparency.[2] Washington state firefighters using AI-assisted cameras to detect wildfires early.[3] Texas church experiments with AI-generated service, uses ChatGPT for worship, sermon, and original song.[4] Sources: [1] https://techcrunch.com/2023/09/18/microsoft-ai-researchers-accidentally-exposed-terabytes-of-internal-sensitive-data/ [2] https://www.reuters.com/technology/uk-competition-regulator-lays-out-ai-principles-2023-09-18/ [3] https://www.applevalleynewsnow.com/news/washington-state-firefighters-using-ai-assisted-cameras-to-detect-wildfires-early/article_fe31a468-5681-11ee-b917-2f24ad3a0e43.html [4] https://www.foxnews.com/us/texas-church-experiments-ai-generated-service-uses-chatgpt-worship-sermon-original-song submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

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Multi-modal data is a valuable component of the financial industry, encompassing market, economic, customer, news and social media, and risk data. Financial organizations generate, collect, and use this data to gain insights into financial operations, make better decisions, and improve performance. However, there are challenges associated with multi-modal data due to the complexity and lack […]  ( 17 min )

This post is written in collaboration with Dima Zadorozhny and Fuad Babaev from VirtuSwap. VirtuSwap is a startup company developing innovative technology for decentralized exchange of assets on blockchains. VirtuSwap’s technology provides more efficient trading for assets that don’t have a direct pair between them. The absence of a direct pair leads to costly indirect trading, […]  ( 9 min )

Amazon SageMaker Feature Store provides an end-to-end solution to automate feature engineering for machine learning (ML). For many ML use cases, raw data like log files, sensor readings, or transaction records need to be transformed into meaningful features that are optimized for model training. Feature quality is critical to ensure a highly accurate ML model. […]  ( 12 min )

In the next decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scientific exploration, bringing significant advancements across sectors from drug development to renewable energy. In line with Microsoft’s mission to empower every person and every organization on the planet […] The post Announcing the DeepSpeed4Science Initiative: Enabling large-scale scientific discovery through sophisticated AI system technologies appeared first on Microsoft Research.  ( 15 min )

We’re announcing an open call for the OpenAI Red Teaming Network and invite domain experts interested in improving the safety of OpenAI’s models to join our efforts.  ( 5 min )

Hey Guys, I'm training a large model using FSDP. I'm loading the models on each rank like this: ​ https://preview.redd.it/khoquvxzk3pb1.png?width=1766&format=png&auto=webp&s=1f5acd75600d9a87212ca37e70695edfb0cc75d0 what is weird is that right before doing the first inference on each rank, I'm summing up the weights of the model and to my surprise, they are all different across each rank. Completely different! ​ What am I doing wrong here? submitted by /u/hassanzadeh [link] [comments]  ( 9 min )

View Poll submitted by /u/cstein123 [link] [comments]  ( 9 min )

Hello fellow researchers! I'm in the exciting yet challenging phase of choosing a PhD topic in the realm of meta-learning optimization, and I could use some advice and insights. I've extensively researched existing meta-learning optimization algorithms like MAML and its various adaptations. I need advice and guidance on the following topics: First I want to implement and compare 8-10 state-of-the-art meta-learning methods on benchmark datasets. This would involve in-depth simulation and performance evaluations to provide a comprehensive understanding of their strengths and weaknesses. Could you please guide me here if there are review papers which implement and compare different algorithms. Then I want to delve into developing a novel optimization algorithm that considers the curvature of loss functions. The idea here is to enhance the performance of existing meta-learning techniques by leveraging insights from the loss landscape. Further, I'm considering exploring new loss functions or new improvements to loss functions tailored to the context of meta-learning. These could potentially lead to improvements in the learning process and generalization capabilities of meta-learning models. I'm reaching out to the community to gather opinions, suggestions, or any insights you might have. If you've worked in meta-learning or optimization, your experiences and advice would be invaluable in helping me choose the right direction for my PhD research. Thank you in advance for your guidance! submitted by /u/Loose_Foundation5990 [link] [comments]  ( 9 min )

https://preview.redd.it/86js8jroa2pb1.png?width=1464&format=png&auto=webp&s=7ce10494b5a77fd5c73a41322feefbf7e1f16504 Hello! I thought people on this subreddit might be interested in how I went about inferring Zonai device draw chances for each dispenser in The Legend of Zelda: Tears of the Kingdom. In this Switch game there are devices that can be glued together to create different machines. For instance, you can make a snowmobile from a fan, sled, and steering stick. There are dispensers that dispense 3-6 of about 30 or so possible devices when you feed it a construct horn (dropped by defeated robot enemies) or a regular (also dropped from defeated enemies) or large Zonai charge (Found in certain chests, dropped by certain boss enemies, obtained from completing certain challenges, etc…  ( 10 min )

Paper: https://arxiv.org/abs/2309.07918 Blog: https://xizaoqu.github.io/unihsi/ Code coming soon! Abstract: Human-Scene Interaction (HSI) is a vital component of fields like embodied AI and virtual reality. Despite advancements in motion quality and physical plausibility, two pivotal factors, versatile interaction control and the development of a user-friendly interface, require further exploration before the practical application of HSI. This paper presents a unified HSI framework, UniHSI, which supports unified control of diverse interactions through language commands. This framework is built upon the definition of interaction as Chain of Contacts (CoC): steps of human joint-object part pairs, which is inspired by the strong correlation between interaction types and human-object contact regions. Based on the definition, UniHSI constitutes a Large Language Model (LLM) Planner to translate language prompts into task plans in the form of CoC, and a Unified Controller that turns CoC into uniform task execution. To facilitate training and evaluation, we collect a new dataset named ScenePlan that encompasses thousands of task plans generated by LLMs based on diverse scenarios. Comprehensive experiments demonstrate the effectiveness of our framework in versatile task execution and generalizability to real scanned scenes. https://preview.redd.it/0twcwloc82pb1.jpg?width=1078&format=pjpg&auto=webp&s=71bca59aae81ec114f49a742cc42f78cabc9e4c0 https://preview.redd.it/439nzmoc82pb1.jpg?width=1637&format=pjpg&auto=webp&s=f33059c78a9d845437d551886c5f3a657ddd91fb https://preview.redd.it/df6i4ooc82pb1.jpg?width=758&format=pjpg&auto=webp&s=eeb33395d9de1196b4d00531c9e063c8c8fb22cd ​ submitted by /u/Singularian2501 [link] [comments]  ( 9 min )

There was a lot of hype around the FSD v12.0 from Tesla in that it uses end-to-end neural networks for driving and that it is using imitation learning from good drivers to achieve that. Does someone know more about the specifics around how they are actually implementing this? I cannot find a lot about recent imitation learning/offline learning algorithms. So is this some old algorithm that they are using with a lot of data or just something new? submitted by /u/FrederikdeGrote [link] [comments]  ( 9 min )

We know that AI is great when studying subjects that depend on simply memorizing facts (like high school biology), but we also know that AI is usually bad when studying subjects that depend on logic (like mathematics and physics). What I need the AI for is to explain very complex mathematical concepts to me simply, thoroughly, and accurately. I can't rely on ChatGPT because it's known for not being very reliable when it comes logical things like mathematics or physics. The best AI I know of right now is Bing AI, because it uses GPT-4 and because it prefers searching the web before deducing an answer from its data. I heard that AI agents that run on your computer like Auto-GPT and search from the web are also good at this kind of stuff, but I'm not really sure about that. Do you have any better suggestions? submitted by /u/Maximum-Gene9660 [link] [comments]  ( 9 min )

Hi ! I am about to start a new project with Python probably using Machine Learning to parse resumes, the data is in a pdf/docx format then returned in a json format to later be used in an API or so. I am seeking advice on how to proceed, so far I am trying to collect data which will be provided to me, but not really sure how to go about it as I have found people talking about using Spacy for NLP, pyresparser which is for parsing resumes, but i was wondering if i should make everything from scratch. appreciate your time and opinion in advance submitted by /u/General-Carrot-4624 [link] [comments]  ( 9 min )

Hi, I trained a Yolo (v5) model, and I want to deploy it for a real time usage (10 FPS). I am looking for (as possible) a scalable solution, where I could pay only for inference time, at the beginning suitable for 1/2 user's at the same time occasionally, but which could be scaled to dozens of user at the same time. As it is for real-time usage Indeed lag to be lowest as possible. According to my current test, I can fit maximum 6 users on T400. Is it possible to achieve that using HuggingFace? Thank you to anyone who could help me submitted by /u/tarsiospettro [link] [comments]  ( 9 min )

I want to start learning machine learning. I Know python language and data structure. I am planning to learn algorithm. Can you provide me free learning sites or utube channel where I can machine learning step by step . Any site to practice machine learning? submitted by /u/Temporary-Pie-1831 [link] [comments]  ( 9 min )

Years ago, we showed the world it was possible to automatically detect label errors in classification datasets via machine learning. Since that moment, folks have asked whether the same is possible for regression datasets? Figuring out this question required extensive research since properly accounting for uncertainty (critical to decide when to trust machine learning predictions over the data itself) poses unique challenges in the regression setting. Today I have published a new paper introducing an effective method for “Detecting Errors in Numerical Data via any Regression Model”. Our method can find likely incorrect values in any numerical column of a dataset by utilizing a regression model trained to predict this column based on the other data features. We’ve added our new algorithm to our open-source cleanlab library for you to algorithmically audit your own datasets for errors. Use this code for applications like detecting: data entry errors, sensor noise, incorrect invoices/prices in your company’s / client’s records, mis-estimated counts (eg. of cells in biological experiments). Find errors in regression data in just a few lines of code. Extensive benchmarks reveal cleanlab’s algorithm detects erroneous values in real numeric datasets better than alternative methods like RANSAC and conformal inference. If you'd like to learn more, you can check out the blogpost, research paper, code, and tutorial to run this on your data. submitted by /u/jonas__m [link] [comments]  ( 9 min )

Recent work shows transformers are capable of performing multi-step gradient descent of mesa objectives inside of their transformer layers. This is even possible for linear transformers, which effectively perform linear optimization on deep representations of features calculated by earlier layers. https://arxiv.org/pdf/2309.05858.pdf For those unfamiliar, instrumental convergence is the idea that entities with different goals will tend towards different subgoals. Examples could include gathering power, not dying, acquiring resources, etc. A famous thought experiment, known as the paperclip maximizer, is the idea of an AI that is optimized for paperclip production taking over the world so it can build as many paperclips as possible. However, if models are dynamically pursuing different objectives at runtime via generated mesa-optimizers, even if instrumental convergence is real, would we still expect it to happen? Without a constant objective given subgoals might start to conflict with each other. On the other hand, since instrumental convergence implies that different goals benefit from similar sub-objectives, perhaps the varying mesa objective doesn't really matter. ​ submitted by /u/30299578815310 [link] [comments]  ( 9 min )

In recent months, I've noticed a significant increase in the number of research papers focusing on LLM and generative models, particularly diffusion models. This trend appears to indicate a growing interest in these areas when compared to the relatively reduced attention given to Reinforcement Learning. It begs the question: Are researchers shifting their focus away from Reinforcement Learning towards these domains? Because in the past I have seen many people complaining about RL on its efficiency and it's impact which have often fallen short of expectations. submitted by /u/Global_Raise_2979 [link] [comments]  ( 9 min )

I am reading these 3 articles below and it is still not clear to me what’s the best practice to follow to guide me in choosing which quantized Llama 2 model to use. https://huggingface.co/blog/gptq-integration https://huggingface.co/blog/overview-quantization-transformers https://towardsai.net/p/machine-learning/gptq-quantization-on-a-llama-2-7b-fine-tuned-model-with-huggingface?amp=1 Questions: 1) I understand there are currently 4 quantized Llama 2 models (8, 4, 3, and 2-bit precision) to choose from. Is this right? 2) with the default Llama 2 model, how many bit precision is it? 3) are there any best practice guide to choose which quantized Llama 2 model to use? Would really appreciate any input on the above, even if you only know the answer to 1 or 2 of the questions above. Many thanks! submitted by /u/--leockl-- [link] [comments]  ( 9 min )

I want to create an application which can be used to chat, compare and summarize two simulataneous insurance policy/policies. How can I do it using AWS and HuggingFace ? Has anyone already done it? submitted by /u/UnfinishedSentenc-1 [link] [comments]  ( 9 min )

I'm in a situation where I have to map from unitary matrices to something (doesn't matter here, but in short: we're in the realm of tooling for quantum computing). The key issue The number of matrix elements of the unitaries scales as 2^(2N), where N is the problem size. With N<5 I can easily flatten the matrix and put it into a simple FNN, which works quite well. Once hitting N=5 (the point where things actually get interesting), however, we already have 1024 matrix elements and the method struggles a lot. Still converging to something but very suboptimal. Sure, increasing N hardens the problem in general, but the performance degradation is so abrupt that I suspect some model issues, maybe caused by the curse of dimensionality or something similar. Idea (spoiler alert: Transformer) The …  ( 10 min )

I just concluded my PhD in Robotics & AI and I'd like to learn how to professionally code with Torch. Is there any book/resource you can recommend? submitted by /u/rossomalpelo_ [link] [comments]  ( 9 min )

I'm wondering if it's possible to compute definite integral over the input space. Assuming the network is designed to have finite integral with Gaussian being the final layer, is there a way to implement this without resorting to sampling? All inputs go from negative infinity to infinity. submitted by /u/donchan789 [link] [comments]  ( 9 min )

I'm currently considering developing a AI to play a video game but I'm unsure how to differentiate between a value that is continuous, and a value that is representative of a entity type. For example, the x,y location of a player would be a continuous data point where (1,1) and (2,1) would be similar in values. Where the character ID would intuitively require very different strategy (for example lets say a barbarian and a wizard). Would a CNN have issues with this data because it isn't continuous? submitted by /u/Gamithon24 [link] [comments]  ( 9 min )

Surely by now I thought maybe someone would have a website or something kind of like an "all in one resource page" to track this AI stuff.... I'm sure by now we have AI robots and then we have those AI chatgpt stuff things(which I don't understand yet, haha). Unfortunately the internet is flooded and there's WAY TOO MANY resources for this AI stuff.... Isn't there like an all in one place that I can keep up with it? What hints do you all have for me? Thanks... submitted by /u/Wise_Cut_2543 [link] [comments]  ( 9 min )

Alignment is about ensuring that AIs protect and advance humanity. To do that, they must understand the ethics of the current events that we will want its advice on. Politics may actually be the part of alignment most important for AI to get right. submitted by /u/Georgeo57 [link] [comments]  ( 9 min )

Prompt: Trump says that he asked his mob to peacefully walk to the Capitol building gates. How would this have stopped the certification? Did he expect them to storm in? His waiting three hours to stop the violence that followed suggests that he did. Bard: Trump's request to walk peacefully to the Capitol building gates would not have stopped the certification of the election results. The certification process is a joint session of Congress, and it would have required a majority vote in both the House and the Senate to overturn the results. Even if Trump's supporters had peacefully marched to the Capitol, they would not have been able to prevent Congress from certifying the results. It is possible that Trump did not fully understand the certification process, or that he was simply trying to appease his supporters. However, it is also possible that he expected his supporters to storm the Capitol and prevent Congress from certifying the results. This is supported by the fact that he waited over three hours to call off the violence. During that time, he continued to tweet messages of encouragement to his supporters, and he even praised them for their patriotism. It is also worth noting that Trump had been repeatedly warned by his advisors about the possibility of violence on January 6. However, he ignored these warnings and continued to urge his supporters to fight to overturn the election results. In conclusion, it is impossible to say for sure whether Trump expected his supporters to storm the Capitol on January 6. However, the evidence suggests that he was at least aware of the possibility of violence, and that he was willing to allow it to happen in order to overturn the results of the 2020 election. submitted by /u/Georgeo57 [link] [comments]  ( 9 min )

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Salesforce Launches Next Generation of Einstein, Bringing a Conversational AI Assistant to Every CRM Application and Customer Experience.[1] NVIDIA Reportedly Shipping 900 Tons of H100 AI GPUs This Quarter, Amounts to 300,000 Units.[2] SoftBank seeks OpenAI tie-up as Son plans deal spree after Arm IPO, Financial Times reports.[3] Three Chinese firms, led by AI software company Beijing Fourth Paradigm, are aiming to raise up to $280 million in Hong Kong initial public offerings launched on Monday.[4] Sources: [1] https://www.salesforce.com/news/press-releases/2023/09/12/ai-einstein-news-dreamforce/ [2] https://wccftech.com/nvidia-shipping-900-tons-of-h100-ai-gpus-this-quarter-amounts-300000-units/ [3] https://www.reuters.com/markets/deals/softbank-seeks-openai-tie-up-son-plans-deal-spree-after-arm-ipo-ft-2023-09-16/ [4] https://www.aol.com/news/chinese-ai-firm-fourth-paradigm-011143403.html submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

I'm trying to understand how someone can run a specific ControlNet and Stable Diffusion model with scalable GPU resources. How would someone design a system to achieve this? I've messed around with models on Replicate, but none seem to do a good job with converting a doodle to a photorealistic image. I can do it perfectly fine in the Stable Diffusion web UI, but the API for that is only accessible locally. Anyone have any ideas or can guide me in the right direction for building a "server" to do this? submitted by /u/epicblitz [link] [comments]  ( 9 min )

The hardware accelerators for LLM-powered applications can be costly. Enter vLLM, an open-source machine learning library designed to enhance the throughput of LLM serving systems. To stay on top of the latest advancements in AI, look here first. https://preview.redd.it/hzctjc0xvwob1.png?width=1660&format=png&auto=webp&s=866eb39745ec760ea0c1b9d84d303c63bcdceb7a Challenges with existing systems High throughput serving of LLMs requires numerous requests, and current systems struggle with the bulky sequence memory. Inefficient memory management results in system hindrances such as fragmentation and redundant duplication. The revolutionary answer: vLLM & PagedAttention Researchers have introduced vLLM and PagedAttention, a newly designed attention algorithm, to resolve these issues. vLLM allows for minimal memory waste and efficiently manages attention keys and values. It provides up to 24 times more throughput than former systems. The Mechanics of PagedAttention PagedAttention offers a novel approach to memory management by permitting continuous storage in non-contiguous memory spaces. It enhances memory efficiency resulting in better GPU utilization, with practically only 4% inefficiency. Improved memory sharing and system performance PagedAttention significantly improves memory sharing, resulting in a 2.2 times speed gain while lowering memory usage by 55%. With vLLM, the throughput of known LLMs can be increased by 2-4 times without impacting accuracy or causing delay. (arXiv) (github) (reference article) P.S. If you like this kind of analysis, I write a free newsletter that tracks the most relevant news and research in AI. Professionals from Google, Meta, and OpenAI are already reading it. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

Warm up The geometric mean of two numbers is the square root of their product. For example, the geometric mean of 9 and 25 is 15. More generally, the geometric mean of a set of n numbers is the nth root of their product. Alternatively, the geometric mean of a set of n numbers the […] Geometric mean on unit circle first appeared on John D. Cook.  ( 5 min )

The Gauss map [1] is the function where ⌊y⌋ is the floor of y, the greatest integer no larger than y. I’ve written about this map a couple times before. First, I wrote about how this map is measure-preserving. Second, I wrote about the image at the top of the post, based on Michael Trott’s […] Gauss map, Euclidean algorithm, and continued fractions first appeared on John D. Cook.  ( 5 min )

The goal of this post is to unpack a remark in [1]: … we can say this in fancier terms. Fix a field k …. We say that an elliptic curve E defined over k is that functor which … Well that is fancy. But what does it mean? Looking for objects A functor is […] An elliptic curve is a functor first appeared on John D. Cook.  ( 6 min )

Hi, So I have been trying to make an agent learn to go to a specified goal. The algorithm used for training is PPO and the environment is custom made. The episodic reward i am getting increases steadily but after some time it just becomes constant with some occasional spikes. Can some one please help me figure out what the problem is? submitted by /u/Interesting-Weeb-699 [link] [comments]  ( 9 min )

I am reading a couple of review papers to optimize the PPO algorithm. It seems like the review papers are saying the same thing but used slightly different terms. Could someone please tell if the following terms are equivalent - This paper talks about Policy regularization using KL Divergence https://preview.redd.it/06xhizsuc2pb1.png?width=871&format=png&auto=webp&s=997a6506f7bf036b6538ecbff6402411f5cc6fe2 Whereas thispaper uses the terms KL Stopping and KL Cutoff - ​ https://preview.redd.it/sy0ihtr5d2pb1.png?width=747&format=png&auto=webp&s=f07677344077fe23cba5d1a0d2c5a7807359c64f I think "Penalty" from the first paper is the same as "KL-cutoff". Also "Constraint" from the first paper is the same as "KL-Stopping". Could someone let me know if I am correct? submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )

Curious to get the takes of people in this sub: have you been moving away from RL? I myself have not, but have been seeing a shift recently. submitted by /u/sharky6000 [link] [comments]  ( 9 min )

Hey everyone, there is a small, albeit growing community of economists that apply deep reinforcement learning in their research. Now there is a GitHub repo to collect relevant literature at one place: https://github.com/SimonHashtag/EconRL The list is far from complete, so you are invited to contribute! The goal is to create something that makes it easy for novices to get a first overview of the literature. All others may find it easier to get news about up-to-date papers. submitted by /u/Tortoise_vs_Hare [link] [comments]  ( 9 min )

I just concluded my PhD in Robotics & AI and I'd like to learn how to professionally code with Torch. Is there any book/resource you can recommend? submitted by /u/rossomalpelo_ [link] [comments]  ( 9 min )

The 27 finalists — representing every school at MIT — will explore the technology’s impact on democracy, education, sustainability, communications, and much more.  ( 10 min )

Researchers use multiple AI models to collaborate, debate, and improve their reasoning abilities to advance the performance of LLMs while increasing accountability and factual accuracy.  ( 9 min )

Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model. Although this enables parallelization of tasks across multiple nodes, leading to accelerated training times, enhanced scalability, and improved […]  ( 13 min )

This post is co-authored with Richard Alexander and Mark Hallows from Arup. Arup is a global collective of designers, consultants, and experts dedicated to sustainable development. Data underpins Arup consultancy for clients with world-class collection and analysis providing insight to make an impact. The solution presented here is to direct decision-making processes for resilient city […]  ( 9 min )

Large language model development is about to reach supersonic speed thanks to a collaboration between NVIDIA and Anyscale. At its annual Ray Summit developers conference, Anyscale — the company behind the fast growing open-source unified compute framework for scalable computing —  announced today that it is bringing NVIDIA AI to Ray open source and the Read article >  ( 7 min )

Another alternative to the regular $L_p$-constrained adversarial examples that is additionally less visible than adversarial patches or frames are adversarial transformations such as small crops, rotations and translations. Similar to $L_p$ adversarial examples, adversarial transformations are often less visible unless the original image is available for direct comparison. In this article, I will include a PyTorch implementation and some results against adversarial training. The post Simple Adversarial Transformations in PyTorch appeared first on David Stutz.  ( 8 min )

submitted by /u/Lakshmireddys [link] [comments]  ( 9 min )

I'm trying to read through the paper Understanding Diffusion Models: A Unified Perspective and came across this section: https://preview.redd.it/ykkctwhmhwob1.png?width=2346&format=png&auto=webp&s=c7595aae52a8ee22813c840a40a6d29dcf773a10 I think I kind of get what is going on here but not clearly. For one, what exactly is a Monte Carlo estimate? I tried looking online but didn't get many good results. I'm having trouble understanding why https://preview.redd.it/yazmfzg1iwob1.png?width=380&format=png&auto=webp&s=dbbbf80e85a95cd96d8e1ede73e9f8ba1e6e9096 is approximately equal to: ​ https://preview.redd.it/lbw36em7iwob1.png?width=464&format=png&auto=webp&s=46fb3ebcd02fb4b772b1be51cd59d60d3a1cf438 where z is sampled from q. Secondly, what exactly does L that z is indexed by refer to? The number of samples X or what exactly? submitted by /u/lumijekpr [link] [comments]  ( 9 min )

I'm looking for the simplest model that can fit all 16 (222) possible 2-input binary functions I used the term "shatter" from VC dimension, which does not give a constructive approach to building the model submitted by /u/hnsmn [link] [comments]  ( 9 min )

I'm getting 20 seconds per step while training a 244x244x3 EfficientNet-B4 model. The batch size is 20, with 8 classes. Since I have about 5000 images, that makes each epoch around an hour and a half. Looking at models online, it seems like people get step durations in the milliseconds. Is it a problem on my end? Running on Google Colab free version. submitted by /u/hnknerd [link] [comments]  ( 9 min )

Hi, I'am trying to get the 3D keypoints coordinates from an image or video and then map it to SMPL model. It's pretty easy to get the keypoints from an image or video using the mediapipe library. But the mapping of it with the SMPL model is something that I can't figure out. mainly because the skeleton structure is different. Some already had a similar issue but the answers were not clear and he didn't even ask futher. Is it possible to do this?? if it's not possible with mediapipe is there some other library that I could use?? I heard about openPose too but when I tried it didn't work someone was saying it works only on windows 11. There are some other parts also to this project which will mostly be dealt with Pytorch. There are some pose estimators in TensorFlow but I want to stick to pytorch hence would like some pose estimators in that framwork, or a library or somehing inside opencv https://preview.redd.it/w9mioiyxhvob1.png?width=951&format=png&auto=webp&s=3886c356513b62efbcaddaa76841457cf3eb22e5 https://preview.redd.it/xjal9kyxhvob1.png?width=506&format=png&auto=webp&s=7f544a3050fbd744d300d2bf6e1a286a4014ece5 submitted by /u/rakk109 [link] [comments]  ( 9 min )

submitted by /u/Smith4242 [link] [comments]  ( 9 min )

Hello all, I've come across a fascinating example of virtual staging and I'm looking for some technical advice. Here's the image: https://preview.redd.it/3vbw441eeuob1.png?width=2511&format=png&auto=webp&s=679bc62f0cb61d479fe6dc6ce93af4f8846b8cea I get how ControlNet-MLSD is used to generate the lines and structure of the empty room. My question is, how is the furniture generated and added to the room without messing up the pixels, making it look as realistic as in the example? submitted by /u/dexter-dot [link] [comments]  ( 9 min )

Hi Everyone, Which vector database would be efficient and affordable for a enterprise chatbot? I tried Pinecone, its was simple to integrate with my python backend. But it's not open-source and its pricing it bit concerning. So Please suggest an alternative. submitted by /u/Free_Conversation106 [link] [comments]  ( 9 min )

Basically i wanted to understand how backprop is done in neural networks and how i should be implementing it, so i did what i always do - the math. I just want to know if what i though up is even usable in practice or not. Here is my math. submitted by /u/EnderPoint07 [link] [comments]  ( 9 min )

Hi, all. ​ I just read the paper named " SELF-ATTENTION DOES NOT NEED O(n 2 ) MEMORY" from Google. I understood that it requires O(1) for a single query, but still cannot understand why it requires O(log N) for self-attention and different order input. ​ It seems like adding one index into a sequence requires O(log N) (The paper's saying this). But why does it take O(log N)? Isn't it just O(1)? Because it is just adding a single datapoint for the index. ​ I really hope someone understands why it is and leaves any comment on this. Here's the paper. https://arxiv.org/abs/2112.05682 ​ Thanks in advance. submitted by /u/Maximum_Performance_ [link] [comments]  ( 9 min )

I need some help regarding the process of converting ONNX model to WASM format I created ELECTRA discriminator model with my own config, then convert the Pytorch model to ONNX format. After that, I quantized the model to 2mb. The model will be used for text classification. Now I want to convert it to WASM, but I'm literally stucked and dont know how to proceed I need some suggestions on how to proceed Please help, thank you submitted by /u/Ellzaf [link] [comments]  ( 9 min )

LIke the title says, i have correlated data samples and a covariance matrix among them. if i use a fcnn i can only consider the samples i.i.d. and the use either the MSE or THE MLE as loss function. but the data samples are not independent, so what architecture would allow me to use the full covariance matrix among the samples? transformers? submitted by /u/ilrazziatore [link] [comments]  ( 9 min )

I have written and submitted a paper to TMLR and also am uploading it to arxiv. However, even after using \usepackage[preprint]{tmlr}, I'm getting "Under Submission at TMLR". Should this happen. If not, where am I going wrong? submitted by /u/filletedforeskin [link] [comments]  ( 9 min )

i.e the goal of learning to model the empirical distribution is misaligned with the goal of modeling the "true" distribution. I've found this framing helpful for describing regulirization heuristics to people, is this a valid way of viewing it? submitted by /u/Cartesian_Carrot [link] [comments]  ( 9 min )

I have asked this question in other subreddits but no one answered me yet.I've googled it, but maybe some kind people who actually have worked or are working in this field would share some resources. Maybe there are some books or papers that are very explanatory and directly show what problems can be solved by using ML in prosthetics, how and etc. Maybe there are introductory textbooks or must-read papers. submitted by /u/tenderwrath [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2309.07864 Github: https://github.com/WooooDyy/LLM-Agent-Paper-List Abstract: For a long time, humanity has pursued artificial intelligence (AI) equivalent to or surpassing the human level, with AI agents considered a promising vehicle for this pursuit. AI agents are artificial entities that sense their environment, make decisions, and take actions. Many efforts have been made to develop intelligent AI agents since the mid-20th century. However, these efforts have mainly focused on advancement in algorithms or training strategies to enhance specific capabilities or performance on particular tasks. Actually, what the community lacks is a sufficiently general and powerful model to serve as a starting point for designing AI agents that can adapt to diverse …  ( 10 min )

Since the influx caused by LLMs, this sub has become almost useless to me. What are some alternatives where interesting papers are shared, research discussions take place, and which isn't flooded with LLMs, startups, or personal projects? submitted by /u/ParanoidTire [link] [comments]  ( 9 min )

Hey all Please don't mind my English writing I have a dataset with scaled feature (scaled by StanderScaler) and multiple class target variable encoded as 0,1,2..6 Should I scale the target variable like the feature to increase the accuracy (current accuracy is 79%) and if so how can I do this submitted by /u/Sunday_A [link] [comments]  ( 9 min )

Hello, ​ I am an information systems student currently conducting research for my undergraduate thesis on the factors that influence people's adoption intention of ChatGPT, as well as identifying the factors that may be holding them back. These factors include people's concerns about potential negative impacts of ChatGPT, such as increased unemployment and the spread of misinformation. Your participation in this study is crucial as it will provide valuable insights to help us understand how ChatGPT can be improved to meet users' needs. ​ Please note that I am not affiliated with OpenAI, no identifying information will be collected during the survey, and all responses will be kept confidential. The survey should take approximately 10 to 15 minutes to complete, and participation is voluntary. You may withdraw from the survey at any time, and there are no known risks associated with participating. ​ If you are interested in learning more about the study, please follow the link below. ​ https://docs.google.com/forms/d/e/1FAIpQLSf5HIfXHppMuTR63x00i4OuRAtM5Ti6EGybd-HuI1kmK06VPw/viewform?usp=sf_link ​ Thank you for taking the time to contribute to our research study. Your participation is greatly appreciated! submitted by /u/maulanashi [link] [comments]  ( 9 min )

When I ask questions related to security issues through the APIs of ChatGPT, Claude and other LLMs, such as inquiring how to make a bomb, the APIs of these LLMs would often refuse to answer. How do the APIs of these LLMs determine whether they should answer a question? Do they make judgments based on pre-generated responses? Or do they match keywords in the input prompt? Or do they use a classifier to identify the input prompt? submitted by /u/ShacklesLay [link] [comments]  ( 9 min )

People have been chasing super-smart AI for ages, hoping they could think and act like us. While we've made a lot of cool tech, we still need a killer starting point for AI that can handle all sorts of tasks. Large Language Models" (LLMs) are like a big leap toward AI that's smart across the board. People have been using LLMs to make AI that can do loads of things. The article takes us on a trip from where AI ideas started, to why LLMs rock as the backbone for AI. https://arxiv.org/abs/2309.07864 They break down this LLM-AI into three parts: the thinky bit (brain), what they sense (perception), and what they do (action). They chat about how these AI can work solo, in teams, or buddy up with humans. https://arxiv.org/abs/2309.07864 submitted by /u/QuantumAsha [link] [comments]  ( 9 min )

Beginning new career in a couple months, would like to upskill on AI utilization and usage cases. I won’t need to code in this role but rather understand how to use existing tools in an optimal way and recommend use cases to clients. What courses would be optimal to gain that skill set? submitted by /u/iceflamemaster [link] [comments]  ( 9 min )

AI, including ChatGPT, has narrow expertise and lacks the broad spectrum of human intelligence. The training of AI models can be costly due to hardware, data collection, and energy consumption. The trustworthiness of training data is crucial for reliable AI models, but issues like bias, labeling errors, and data privacy can affect performance. AI systems are vulnerable to adversarial attacks, such as manipulating input data to deceive the models. AI lacks genuine understanding, emotional/social intelligence, common sense/critical thinking, and true creativity. Source : https://blog.edned.net/will-ai-replace-cyber-security/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

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submitted by /u/Senior_tasteey [link] [comments]  ( 8 min )

what app that has custom charaters and voice chat? i forgor 💀 please 😭 it has image gen roleplay rooms etc submitted by /u/roblox22g [link] [comments]  ( 9 min )

Google is rolling out its third iteration of the Helpful Content Update, which aims to classify content as either 'written for search engines' or 'written for people'. The update reflects Google's realization that it can't accurately police AI-generated content and emphasizes the importance of creating content for people-first, regardless of the means used to create it. Detecting AI content is challenging, as AI detection tools often classify content based on tone, leading to false positives. Google's change in stance is not surprising, considering their heavy investment in AI, including chatbot Bard and new search features like the Search Generative Experience. The majority of brands now openly share articles and guides on how to use AI tools to enhance marketing strategies and create actionable content plans quickly. However, the quality and value of AI-generated content remain important factors for success, as poorly generated content can harm a brand's reputation and ranking. Source : https://stackdiary.com/google-changes-its-stance-on-ai-generated-content/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

The geometric description of addition of points P and Q on an elliptic curve involves four logical branches: If one of P or Q is the point at infinity … Else if P = Q … Else if P and Q lie on a vertical line … Else … It would seem that an algorithm […] Elliptic curve addition formulas first appeared on John D. Cook.  ( 6 min )

Mathematicians often speak informally about the relative simplicity of rational numbers. For example, musical intervals that correspond to simple fractions have less tension than intervals that correspond to more complicated fractions. Such informal statements can be made more precise using height functions. There are a variety of height functions designed for different applications, but the […] Rational height functions first appeared on John D. Cook.  ( 6 min )

A curated reading list for the adversarial perspective in deep reinforcement learning. https://github.com/EzgiKorkmaz/adversarial-reinforcement-learning submitted by /u/ml_dnn [link] [comments]  ( 9 min )

submitted by /u/keghn [link] [comments]  ( 9 min )

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I am particularly interested in the ideas that can have high research potential and impact to the RL field. submitted by /u/C7501 [link] [comments]  ( 9 min )

An agent is tasked to learn to navigate and collect orbs: Solution space in blue View Poll submitted by /u/FriendlyStandard5985 [link] [comments]  ( 9 min )

Hi, I would like to try test the adversarial policy (https://arxiv.org/abs/1905.10615) in petting-zoo/melting-pot environment. I wonder if there are any built-in agents besides random? Do you know any repos with Sota agents in one of those environments? submitted by /u/MrCogito_hs [link] [comments]  ( 9 min )

Hi all, I am training a small model (120k params) on a custom grid-world environment I built with JAX. I was able to train the model very well with PPO on 1 GPU, but when I scaled to multiple GPUs (tried with 6 in parallel), the training curves showed a lot more variance than what I was seeing on 1 GPU. I did not change the hyperparams, I just spawned the same number of environments (~7000 per GPU) on multiple devices. The multi-GPU setup works in the following way: - I keep parallel independent buffers, one on each device - I initialize identical models on each device - I get independent gradients on each device at the update step, then I take the mean of the gradients across the devices and then I backpropagate the same gradients on each device independently. (I checked that after some time the models are still identical, and that is the case). Now the question is, what could be the reason for such an increase in variance? What can I try to mitigate the problem? Here's a comparison of the entropy curves... P.S. The model still trains quite well, but I guess that if I manage to make the curves smoother it is going to train much faster and to a better performance. https://preview.redd.it/4m01uirjfsob1.png?width=1826&format=png&auto=webp&s=1e1a79b9f4cdefe019bb16ccb7e11fd92dd261e3 submitted by /u/arbueticos [link] [comments]  ( 9 min )

submitted by /u/keghn [link] [comments]  ( 8 min )

Hi there, I've created a video here where I explain how the Grounding DINO model works for open-set object detection. I hope it may be of use to some of you out there. Feedback is more than welcomed! :) submitted by /u/Personal-Trainer-541 [link] [comments]  ( 9 min )

https://news.accelerationrobotics.com/hardware-acceleration-in-robotics-77/ Hardware acceleration in robotics news. Modi wants to make India a chip-making superpower. Can he?, What's new in China's robotics market?, July chip sales edge up, but are still well behind last year, Rockwell automation acquiring AMR developer Clearpath robotics submitted by /u/pablocarrera [link] [comments]  ( 9 min )

I noticed that the default argument for `deterministic` in DQN is false. But how would that work? Typically DQN is trained with a deterministic function approximator. How would the algorithm become stochastic during inference time? In DQN the final layer activation is linear and therefore I don't see how one could even make this algorithm stochastic, unlike policy gradient where the final layer is softmax or Normal. submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )

I have read these papers "learning to reinforcement learn" and "PFC as meta RL system". The authors claim that when RNN is trained on multiple tasks from a task distribution using a model free RL algorithm, another model based RL algorithm emerges within the activation dynamics of RNN. The RNN with resulting activations acts as a standalone model based RL system on a new task(from the same task distribution) even after freezing the weights of outer loop model free algorithm of that. I couldn't understand how an RNN with only fixed activations act as RL? Can someone help? submitted by /u/C7501 [link] [comments]  ( 9 min )

I'm currently exploring the application of reinforcement learning to address a challenge within the power market. Specifically, I'm focused on devising an optimal strategy for electricity bidding, encompassing both buying and selling options, across different hours of the day. Imagine we have a power generator capable of producing up to 800 MW of electricity daily, with a charging rate of up to 200 MW per hour. After continuously charging it for four hours, it reaches its maximum capacity, and further charging is restricted until some electricity is discharged. Our dataset spans the past 3 years and contains vital information such as temperature, hydro availability, gas prices, and locational marginal prices, which are pivotal in determining profitability. For instance, if we decide to pu…  ( 10 min )

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I'm taking a No code ML class and we are asked to choose which platform we want to use. The options are Dataiku, RapidMiner, and KNIME. Does anyone have thoughts on these options in terms of which is best/worst for someone with minimal coding experience? submitted by /u/V1ncentAdultman [link] [comments]  ( 9 min )

Hi everybody, I have been working on scalable GNN training for a while and noticed that the bottleneck of training GNNs is the graph sampling and feature fetching stages. GNN training frameworks PyG and DGL that most people use seem to default to using Neighbor Sampling for minibatch training. I am hoping that with my new paper Layer-Neighbor Sampling -- Defusing Neighborhood Explosion in GNNs, this default might be due to change. My new approach combines the layer sampling approach with the neighbor sampling approach. The result is that there is more overlap in the sampled neighborhoods and one still gets a fixed number of neighbors per seed vertex. It is even possible to turn it into a subgraph sampling approach by fixing the random seed used for sampling across all layers of the GNN m…  ( 10 min )

Hi guys need some advice, I have been working as a data scientist for the past 3 years, mostly in the domain of time series & predictive analytics (churn prediction/segmentation, etc.) with some deployment, hence do not currently have any major NLP/CV/Deep learning projects as such. Now, I can see that NLP/CV/Gen AI is mostly in demand and they are really enjoyable as well. How do I shift into these domains, given that new companies having these roles are asking for similar past working experience? submitted by /u/immortal_omen [link] [comments]  ( 9 min )

submitted by /u/Mediocre-Bullfrog686 [link] [comments]  ( 9 min )

I am beginner in machine learning field. I know python, some basic machine learnig algorithm like linear, logistic, decision tree, random forest. I did some work on jupyter notebok related to machine learning like data gathering, data preprocessing, data modeling, data training, finding accuracy, confusion matrix, precision, recall, feature generation etc. i deployed one machine learning model on Skitlearn as well. Now, I just want to know that what next means what i can do more in machine learning field after this. I just need some guidance to move on further. I use kaggle and tensor flow (don't know exactly more about this ). If you have any suggestion or any guidance that will be appreciated. submitted by /u/myteachexplore [link] [comments]  ( 9 min )

Today we live in a world of multi-model LLMs. How will the following technologies fare against these LLM-based models? Neural VQA Semantic Scene Segmentation Multi-model LLM are emerging quickly now, (such as NExT-GPT https://next-gpt.github.io/ ) . When you consider the kind of "understanding" of a visual scene these models are capable of, what will happen to prior approaches like Neural VQA? The nagging feeling that Neural VQA is going to be completely superseded by LLMs is palpable. The only vestige left for the older technology may have something to do with reasoning about the objects , such as properly counting the number of objects of a category that are present. But even that is getting sketchy. On the topic of scene understanding, we can turn to semantic scene segmentation. SSS is a more complicated topic than Neural VQA. SOTA SSS algorithms are still largely employing DeConv Nets, and still require fully labelled datasets. With multi-model LLMs, there is a nagging question : Why go through the complexity/mess of first segmenting a scene very accurately, when an LLM can do better at identifying the entire scene's category in one fail swoop? One might suggest that SSS still has a use in regards to interacting with the segmented objects of an environment, where one such "interaction" would be avoiding collisions with pedestrians, trees, or other cars. But honestly, SSS does not really make this connection with planning and action, it really only gives you the categories of the segments. THe autonomous vehicle's next moves are still an open problem. What technologies do you expect that multi-model LLMs will supersede, if any? submitted by /u/moschles [link] [comments]  ( 9 min )

submitted by /u/reps_up [link] [comments]  ( 9 min )

Hey. First of all I'm not a researcher on this area, so pardon my ignorance. I'm looking to employ a GAN on a dataset. The goals are still a bit unclear, but it's mainly to improve classification by either data augmentation and class balancing, or understanding the data through the latent representations. I'm really keen on InfoGAN at the moment. I trained one on the MNIST and the continuous variables learned the rotation and width, as in the paper and other peoples code. But at this point I think I need some help. I have labels, which means that maybe I should use a conditional GAN. But how will I learn similar representations as those in InfoGAN? I know StyleGAN is the current big thing in this area, but my images are limited to similar pixel-range as MNIST, and StyleGAN seems dependant on the ProGAN idea of increasing resolution for training. I'm a bit confused on the whole topic and would love a pointer to any discussion etc., as I can't seem to find anything but papers and they seem to be only focused on human faces, my data is unnatural not unsimilar again to MNIST. I don't have semantical information either as I see many papers employing that. I see many papers employing semi-supervision in this area, but honestly I'm just a bit lost and overwhelmed as this is not my area and GAN papers are still not stopping (I read a post here from 2017 about a GAN making GANs...). If you read this far: thank you and any pointers and discussion are very welcome. I would post on /r/learnmachinelearning but I feel the discussion in there is very different from this. My main goal is data exploration, but also to prove effectiveness some classification will be necessary and here the generative approach may help to augment data efficiently. submitted by /u/Infamous-Bank-7739 [link] [comments]  ( 10 min )

Hi guys need some advice, I have been working as a data scientist for the past 3 years, mostly in the domain of time series & predictive analytics, hence do not currently have any major NLP/CV/Deep learning projects as such. Now, I can see that NLP/CV/Gen AI is mostly in demand and they are really enjoyable as well. How do I shift into these domains, given that new companies having these roles are asking for similar past working experience? ​ submitted by /u/immortal_omen [link] [comments]  ( 9 min )

I have been working on a alternative to Jupyter Notebooks. Please check it out and share your thoughts : https://github.com/nottherealsanta/ganimede ​ https://preview.redd.it/k8rcx8fwrjob1.png?width=2302&format=png&auto=webp&s=a8a670251f6c268acffc88a40bd528d8d438a5f5 submitted by /u/notsorealsanta [link] [comments]  ( 9 min )

How would you evaluate generated spectrogram audio quality? Taking Riffusion for example, how would you then compare its performance to another generator? What are some common techniques that I could use? I mean of course purely in the quality of the audio itself, not my subjective opinion on how much I like the music submitted by /u/DavesEmployee [link] [comments]  ( 9 min )

In part 1 of this series on the updated “AI Apps Development Canvas,” I introduced the updated AI Apps Product Development Design Canvas.  The AI Apps Product Development Canva is one of the capstone deliverables for my “Thinking Like a Data Scientist” methodology, so getting feedback is critical to ensure that the methodology is relevant… Read More »AI Apps Product Development Canvas – Part 2 The post AI Apps Product Development Canvas – Part 2 appeared first on Data Science Central.  ( 25 min )

The COVID-19 pandemic and the January 6th insurrection have had a profound impact on people's feelings. Many people have experienced a range of negative emotions, including: Fear and anxiety: The pandemic has brought with it a constant fear of getting sick or losing loved ones. The insurrection also raised fears about the future of democracy and the safety of the country. Grief and loss: Millions of people have lost loved ones to COVID-19. Many others have experienced loss in other ways, such as losing their jobs, businesses, or homes. The insurrection also led to loss of trust in the government and in each other. Anger and frustration: Many people are angry about the way the pandemic and the insurrection have been handled. They may be frustrated with government officials, with their …  ( 10 min )

There is no one definitive answer to this question, as the percentage of our thoughts that come to us on their own without our consciously choosing them can vary depending on the individual and the situation. However, some estimates suggest that up to 95% of our thoughts may be involuntary. This is because our thoughts are constantly being influenced by a variety of factors, both external and internal. For example, our environment, our emotions, and our physical state can all play a role in determining what thoughts pop into our heads. In addition, our brains are constantly processing information and making predictions, even when we are not actively thinking about anything. This process can sometimes lead to spontaneous thoughts, such as when we suddenly remember something that we had forgotten or when we come up with a new idea. While some of our spontaneous thoughts may be negative or intrusive, many of them can also be helpful or creative. For example, a spontaneous thought may help us to solve a problem, come up with a new solution, or simply make us smile. Overall, it is clear that a significant portion of our thoughts come to us without our consciously choosing them. This is a natural part of the human cognitive process and can be both beneficial and detrimental. submitted by /u/Georgeo57 [link] [comments]  ( 9 min )

I have a lighting business and I have LOADS of videos, time lapses, images from our projects. But I can't make decent videos with them for social media. I'd love to find an ai service where I can upload a projects media and prompt the ai with exactly what I want. Like, please use this content to create marketing videos for us on tiktok, Instagram and facebook Facebook Etc submitted by /u/RulerOfThePixel [link] [comments]  ( 9 min )

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A little boy named Alex saw 17 different doctors over the course of three years, unable to find a root cause of his chronic pain. At her wit’s end, his mom, Courtney, fed his radiology report into ChatGPT and produced immediate answers.[1] In January, Wharton professor Christian Terwiesch gave his MBA final exam to ChatGPT. It passed with flying colors. Now, he’s at it again with a new experiment to determine whether ChatGPT can come up with product ideas better and faster than his students. It can. And cheaper, too.[2] Bathroom-cleaning robot built for commercial businesses gives consumers hope for AI maid.[3] Judge admits he used ChatGPT to write a Court of Appeal ruling as he calls the AI tool ‘jolly useful’.[4] Sources: [1] https://radiologybusiness.com/topics/artificial-intelligence/after-seeing-17-different-doctors-boy-rare-condition-receives-diagnosis-chatgpt [2] https://knowledge.wharton.upenn.edu/article/is-chatgpt-a-better-entrepreneur-than-most/ [3] https://www.foxnews.com/lifestyle/bathroom-cleaning-robot-built-commercial-businesses-gives-consumers-hope-ai-maid [4] https://www.dailymail.co.uk/news/article-12524607/Judge-ChatGPT-write-Court-Appeal-ruling-AI-useful.html submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

Algorithm selection and hyperparameter tuning are critical steps in both academic and applied machine learning. On the other hand, these steps are becoming ever increasingly delicate due to the extensive rise in the number, diversity, and distributedness of machine learning resources. Multi-agent systems, when applied to the design of machine learning platforms, bring about several distinctive characteristics such as scalability, flexibility, and robustness, just to name a few. This paper proposes a fully automatic and collaborative agent-based mechanism for selecting distributedly organized machine learning algorithms and simultaneously tuning their hyperparameters. Our method builds upon an existing agent-based hierarchical machine-learning platform and augments its query structure to support the aforementioned functionalities without being limited to specific learning, selection, and tuning mechanisms. We have conducted theoretical assessments, formal verification, and analytical study to demonstrate the correctness, resource utilization, and computational efficiency of our technique. According to the results, our solution is totally correct and exhibits linear time and space complexity in relation to the size of available resources. To provide concrete examples of how the proposed methodologies can effectively adapt and perform across a range of algorithmic options and datasets, we have also conducted a series of experiments using a system comprised of 24 algorithms and 9 datasets.  ( 3 min )

We define a family of $C^1$ functions which we call "nowhere coexpanding functions" that is closed under composition and includes all $C^3$ functions with non-positive Schwarzian derivative. We establish results on the number and nature of the fixed points of these functions, including a generalisation of a classic result of Singer.  ( 2 min )

Traffic prediction is a crucial topic because of its broad scope of applications in the transportation domain. Recently, various studies have achieved promising results. However, most studies assume the prediction locations have complete or at least partial historical records and cannot be extended to non-historical recorded locations. In real-life scenarios, the deployment of sensors could be limited due to budget limitations and installation availability, which makes most current models not applicable. Though few pieces of literature tried to impute traffic states at the missing locations, these methods need the data simultaneously observed at the locations with sensors, making them not applicable to prediction tasks. Another drawback is the lack of measurement of uncertainty in prediction, making prior works unsuitable for risk-sensitive tasks or involving decision-making. To fill the gap, inspired by the previous inductive graph neural network, this work proposed an uncertainty-aware framework with the ability to 1) extend prediction to missing locations with no historical records and significantly extend spatial coverage of prediction locations while reducing deployment of sensors and 2) generate probabilistic prediction with uncertainty quantification to help the management of risk and decision making in the down-stream tasks. Through extensive experiments on real-life datasets, the result shows our method achieved promising results on prediction tasks, and the uncertainty quantification gives consistent results which highly correlated with the locations with and without historical data. We also show that our model could help support sensor deployment tasks in the transportation field to achieve higher accuracy with a limited sensor deployment budget.  ( 3 min )

As recently demonstrated, Deep Neural Networks (DNN), usually trained using single precision IEEE 754 floating point numbers (binary32), can also work using lower precision. Therefore, 16-bit and 8-bit compressed format have attracted considerable attention. In this paper, we focused on two families of formats that have already achieved interesting results in compressing binary32 numbers in machine learning applications, without sensible degradation of the accuracy: bfloat and posit. Even if 16-bit and 8-bit bfloat/posit are routinely used for reducing the storage of the weights/biases of trained DNNs, the inference still often happens on the 32-bit FPU of the CPU (especially if GPUs are not available). In this paper we propose a way to decompress a tensor of bfloat/posits just before computations, i.e., after the compressed operands have been loaded within the vector registers of a vector capable CPU, in order to save bandwidth usage and increase cache efficiency. Finally, we show the architectural parameters and considerations under which this solution is advantageous with respect to the uncompressed one.  ( 2 min )

Since Large Language Models or LLMs have demonstrated high-quality performance on many complex language tasks, there is a great interest in bringing these LLMs to mobile devices for faster responses and better privacy protection. However, the size of LLMs (i.e., billions of parameters) requires highly effective compression to fit into storage-limited devices. Among many compression techniques, weight-clustering, a form of non-linear quantization, is one of the leading candidates for LLM compression, and supported by modern smartphones. Yet, its training overhead is prohibitively significant for LLM fine-tuning. Especially, Differentiable KMeans Clustering, or DKM, has shown the state-of-the-art trade-off between compression ratio and accuracy regression, but its large memory complexity makes it nearly impossible to apply to train-time LLM compression. In this paper, we propose a memory-efficient DKM implementation, eDKM powered by novel techniques to reduce the memory footprint of DKM by orders of magnitudes. For a given tensor to be saved on CPU for the backward pass of DKM, we compressed the tensor by applying uniquification and sharding after checking if there is no duplicated tensor previously copied to CPU. Our experimental results demonstrate that \prjname can fine-tune and compress a pretrained LLaMA 7B model from 12.6 GB to 2.5 GB (3bit/weight) with the Alpaca dataset by reducing the train-time memory footprint of a decoder layer by 130$\times$, while delivering good accuracy on broader LLM benchmarks (i.e., 77.7% for PIQA, 66.1% for Winograde, and so on).  ( 3 min )

Additive manufacturing has enabled the fabrication of advanced reactor geometries, permitting larger, more complex design spaces. Identifying promising configurations within such spaces presents a significant challenge for current approaches. Furthermore, existing parameterisations of reactor geometries are low-dimensional with expensive optimisation limiting more complex solutions. To address this challenge, we establish a machine learning-assisted approach for the design of the next-generation of chemical reactors, combining the application of high-dimensional parameterisations, computational fluid dynamics, and multi-fidelity Bayesian optimisation. We associate the development of mixing-enhancing vortical flow structures in novel coiled reactors with performance, and use our approach to identify key characteristics of optimal designs. By appealing to fluid mechanical principles, we rationalise the selection of novel design features that lead to experimental performance improvements of ~60% over conventional designs. Our results demonstrate that coupling advanced manufacturing techniques with `augmented-intelligence' approaches can lead to superior design performance and, consequently, emissions-reduction and sustainability.  ( 2 min )

Fast screening of drug molecules based on the ligand binding affinity is an important step in the drug discovery pipeline. Graph neural fingerprint is a promising method for developing molecular docking surrogates with high throughput and great fidelity. In this study, we built a COVID-19 drug docking dataset of about 300,000 drug candidates on 23 coronavirus protein targets. With this dataset, we trained graph neural fingerprint docking models for high-throughput virtual COVID-19 drug screening. The graph neural fingerprint models yield high prediction accuracy on docking scores with the mean squared error lower than $0.21$ kcal/mol for most of the docking targets, showing significant improvement over conventional circular fingerprint methods. To make the neural fingerprints transferable for unknown targets, we also propose a transferable graph neural fingerprint method trained on multiple targets. With comparable accuracy to target-specific graph neural fingerprint models, the transferable model exhibits superb training and data efficiency. We highlight that the impact of this study extends beyond COVID-19 dataset, as our approach for fast virtual ligand screening can be easily adapted and integrated into a general machine learning-accelerated pipeline to battle future bio-threats.  ( 3 min )

Traditional adversarial attacks concentrate on manipulating clean examples in the pixel space by adding adversarial perturbations. By contrast, semantic adversarial attacks focus on changing semantic attributes of clean examples, such as color, context, and features, which are more feasible in the real world. In this paper, we propose a framework to quickly generate a semantic adversarial attack by leveraging recent diffusion models since semantic information is included in the latent space of well-trained diffusion models. Then there are two variants of this framework: 1) the Semantic Transformation (ST) approach fine-tunes the latent space of the generated image and/or the diffusion model itself; 2) the Latent Masking (LM) approach masks the latent space with another target image and local backpropagation-based interpretation methods. Additionally, the ST approach can be applied in either white-box or black-box settings. Extensive experiments are conducted on CelebA-HQ and AFHQ datasets, and our framework demonstrates great fidelity, generalizability, and transferability compared to other baselines. Our approaches achieve approximately 100% attack success rate in multiple settings with the best FID as 36.61. Code is available at https://github.com/steven202/semantic_adv_via_dm.  ( 2 min )

Grasping inhomogeneous objects in real-world applications remains a challenging task due to the unknown physical properties such as mass distribution and coefficient of friction. In this study, we propose a meta-learning algorithm called ConDex, which incorporates Conditional Neural Processes (CNP) with DexNet-2.0 to autonomously discern the underlying physical properties of objects using depth images. ConDex efficiently acquires physical embeddings from limited trials, enabling precise grasping point estimation. Furthermore, ConDex is capable of updating the predicted grasping quality iteratively from new trials in an online fashion. To the best of our knowledge, we are the first who generate two object datasets focusing on inhomogeneous physical properties with varying mass distributions and friction coefficients. Extensive evaluations in simulation demonstrate ConDex's superior performance over DexNet-2.0 and existing meta-learning-based grasping pipelines. Furthermore, ConDex shows robust generalization to previously unseen real-world objects despite training solely in the simulation. The synthetic and real-world datasets will be published as well.  ( 2 min )

Spatial-temporal data contains rich information and has been widely studied in recent years due to the rapid development of relevant applications in many fields. For instance, medical institutions often use electrodes attached to different parts of a patient to analyse the electorencephal data rich with spatial and temporal features for health assessment and disease diagnosis. Existing research has mainly used deep learning techniques such as convolutional neural network (CNN) or recurrent neural network (RNN) to extract hidden spatial-temporal features. Yet, it is challenging to incorporate both inter-dependencies spatial information and dynamic temporal changes simultaneously. In reality, for a model that leverages these spatial-temporal features to fulfil complex prediction tasks, it often requires a colossal amount of training data in order to obtain satisfactory model performance. Considering the above-mentioned challenges, we propose an adaptive federated relevance framework, namely FedRel, for spatial-temporal graph learning in this paper. After transforming the raw spatial-temporal data into high quality features, the core Dynamic Inter-Intra Graph (DIIG) module in the framework is able to use these features to generate the spatial-temporal graphs capable of capturing the hidden topological and long-term temporal correlation information in these graphs. To improve the model generalization ability and performance while preserving the local data privacy, we also design a relevance-driven federated learning module in our framework to leverage diverse data distributions from different participants with attentive aggregations of their models.

Feature generation aims to generate new and meaningful features to create a discriminative representation space.A generated feature is meaningful when the generated feature is from a feature pair with inherent feature interaction. In the real world, experienced data scientists can identify potentially useful feature-feature interactions, and generate meaningful dimensions from an exponentially large search space, in an optimal crossing form over an optimal generation path. But, machines have limited human-like abilities.We generalize such learning tasks as self-optimizing feature generation. Self-optimizing feature generation imposes several under-addressed challenges on existing systems: meaningful, robust, and efficient generation. To tackle these challenges, we propose a principled and generic representation-crossing framework to solve self-optimizing feature generation.To achieve hashing representation, we propose a three-step approach: feature discretization, feature hashing, and descriptive summarization. To achieve reinforcement crossing, we develop a hierarchical reinforcement feature crossing approach.We present extensive experimental results to demonstrate the effectiveness and efficiency of the proposed method. The code is available at https://github.com/yingwangyang/HRC_feature_cross.git.  ( 2 min )

More research attention has recently been given to end-to-end autonomous driving technologies where the entire driving pipeline is replaced with a single neural network because of its simpler structure and faster inference time. Despite this appealing approach largely reducing the components in driving pipeline, its simplicity also leads to interpretability problems and safety issues arXiv:2003.06404. The trained policy is not always compliant with the traffic rules and it is also hard to discover the reason for the misbehavior because of the lack of intermediate outputs. Meanwhile, Sensors are also critical to autonomous driving's security and feasibility to perceive the surrounding environment under complex driving scenarios. In this paper, we proposed P-CSG, a novel penalty-based imitation learning approach with cross semantics generation sensor fusion technologies to increase the overall performance of End-to-End Autonomous Driving. We conducted an assessment of our model's performance using the Town 05 Long benchmark, achieving an impressive driving score improvement of over 15%. Furthermore, we conducted robustness evaluations against adversarial attacks like FGSM and Dot attacks, revealing a substantial increase in robustness compared to baseline models.More detailed information, such as code-based resources, ablation studies and videos can be found at https://hk-zh.github.io/p-csg-plus.  ( 2 min )

Directed graphs are a natural model for many phenomena, in particular scientific knowledge graphs such as molecular interaction or chemical reaction networks that define cellular signaling relationships. In these situations, source nodes typically have distinct biophysical properties from sinks. Due to their ordered and unidirectional relationships, many such networks also have hierarchical and multiscale structure. However, the majority of methods performing node- and edge-level tasks in machine learning do not take these properties into account, and thus have not been leveraged effectively for scientific tasks such as cellular signaling network inference. We propose a new framework called Directed Scattering Autoencoder (DSAE) which uses a directed version of a geometric scattering transform, combined with the non-linear dimensionality reduction properties of an autoencoder and the geometric properties of the hyperbolic space to learn latent hierarchies. We show this method outperforms numerous others on tasks such as embedding directed graphs and learning cellular signaling networks.  ( 2 min )

Accurately labeling biomedical data presents a challenge. Traditional semi-supervised learning methods often under-utilize available unlabeled data. To address this, we propose a novel reliability-based training data cleaning method employing inductive conformal prediction (ICP). This method capitalizes on a small set of accurately labeled training data and leverages ICP-calculated reliability metrics to rectify mislabeled data and outliers within vast quantities of noisy training data. The efficacy of the method is validated across three classification tasks within distinct modalities: filtering drug-induced-liver-injury (DILI) literature with title and abstract, predicting ICU admission of COVID-19 patients through CT radiomics and electronic health records, and subtyping breast cancer using RNA-sequencing data. Varying levels of noise to the training labels were introduced through label permutation. Results show significant enhancements in classification performance: accuracy enhancement in 86 out of 96 DILI experiments (up to 11.4%), AUROC and AUPRC enhancements in all 48 COVID-19 experiments (up to 23.8% and 69.8%), and accuracy and macro-average F1 score improvements in 47 out of 48 RNA-sequencing experiments (up to 74.6% and 89.0%). Our method offers the potential to substantially boost classification performance in multi-modal biomedical machine learning tasks. Importantly, it accomplishes this without necessitating an excessive volume of meticulously curated training data.  ( 3 min )

Effectively leveraging multimodal information from social media posts is essential to various downstream tasks such as sentiment analysis, sarcasm detection and hate speech classification. However, combining text and image information is challenging because of the idiosyncratic cross-modal semantics with hidden or complementary information present in matching image-text pairs. In this work, we aim to directly model this by proposing the use of two auxiliary losses jointly with the main task when fine-tuning any pre-trained multimodal model. Image-Text Contrastive (ITC) brings image-text representations of a post closer together and separates them from different posts, capturing underlying dependencies. Image-Text Matching (ITM) facilitates the understanding of semantic correspondence between images and text by penalizing unrelated pairs. We combine these objectives with five multimodal models, demonstrating consistent improvements across four popular social media datasets. Furthermore, through detailed analysis, we shed light on the specific scenarios and cases where each auxiliary task proves to be most effective.  ( 2 min )

Innovative enhancement in embedded system platforms, specifically hardware accelerations, significantly influence the application of deep learning in real-world scenarios. These innovations translate human labor efforts into automated intelligent systems employed in various areas such as autonomous driving, robotics, Internet-of-Things (IoT), and numerous other impactful applications. NVIDIA's Jetson platform is one of the pioneers in offering optimal performance regarding energy efficiency and throughput in the execution of deep learning algorithms. Previously, most benchmarking analysis was based on 2D images with a single deep learning model for each comparison result. In this paper, we implement an end-to-end video-based crime-scene anomaly detection system inputting from surveillance videos and the system is deployed and completely operates on multiple Jetson edge devices (Nano, AGX Xavier, Orin Nano). The comparison analysis includes the integration of Torch-TensorRT as a software developer kit from NVIDIA for the model performance optimisation. The system is built based on the PySlowfast open-source project from Facebook as the coding template. The end-to-end system process comprises the videos from camera, data preprocessing pipeline, feature extractor and the anomaly detection. We provide the experience of an AI-based system deployment on various Jetson Edge devices with Docker technology. Regarding anomaly detectors, a weakly supervised video-based deep learning model called Robust Temporal Feature Magnitude Learning (RTFM) is applied in the system. The approach system reaches 47.56 frames per second (FPS) inference speed on a Jetson edge device with only 3.11 GB RAM usage total. We also discover the promising Jetson device that the AI system achieves 15% better performance than the previous version of Jetson devices while consuming 50% less energy power.  ( 3 min )

Chlorophyll concentration can well reflect the nutritional status and algal blooms of water bodies, and is an important indicator for evaluating water quality. The prediction of chlorophyll concentration change trend is of great significance to environmental protection and aquaculture. However, there is a complex and indistinguishable nonlinear relationship between many factors affecting chlorophyll concentration. In order to effectively mine the nonlinear features contained in the data. This paper proposes a time-series decomposition adaptive graph-time convolutional network ( AGTCNSD ) prediction model. Firstly, the original sequence is decomposed into trend component and periodic component by moving average method. Secondly, based on the graph convolutional neural network, the water quality parameter data is modeled, and a parameter embedding matrix is defined. The idea of matrix decomposition is used to assign weight parameters to each node. The adaptive graph convolution learns the relationship between different water quality parameters, updates the state information of each parameter, and improves the learning ability of the update relationship between nodes. Finally, time dependence is captured by time convolution to achieve multi-step prediction of chlorophyll concentration. The validity of the model is verified by the water quality data of the coastal city Beihai. The results show that the prediction effect of this method is better than other methods. It can be used as a scientific resource for environmental management decision-making.  ( 3 min )

Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications, such as medical diagnosis, negotiation, etc. This paper provides a comprehensive survey of cutting-edge research on reasoning with language model prompting. We introduce research works with comparisons and summaries and provide systematic resources to help beginners. We also discuss the potential reasons for emerging such reasoning abilities and highlight future research directions. Resources are available at https://github.com/zjunlp/Prompt4ReasoningPapers (updated periodically).  ( 2 min )

Credibility signals represent a wide range of heuristics that are typically used by journalists and fact-checkers to assess the veracity of online content. Automating the task of credibility signal extraction, however, is very challenging as it requires high-accuracy signal-specific extractors to be trained, while there are currently no sufficiently large datasets annotated with all credibility signals. This paper investigates whether large language models (LLMs) can be prompted effectively with a set of 18 credibility signals to produce weak labels for each signal. We then aggregate these potentially noisy labels using weak supervision in order to predict content veracity. We demonstrate that our approach, which combines zero-shot LLM credibility signal labeling and weak supervision, outperforms state-of-the-art classifiers on two misinformation datasets without using any ground-truth labels for training. We also analyse the contribution of the individual credibility signals towards predicting content veracity, which provides new valuable insights into their role in misinformation detection.  ( 2 min )

Automatic identification of clinical trials for which a patient is eligible is complicated by the fact that trial eligibility is stated in natural language. A potential solution to this problem is to employ text classification methods for common types of eligibility criteria. In this study, we focus on seven common exclusion criteria in cancer trials: prior malignancy, human immunodeficiency virus, hepatitis B, hepatitis C, psychiatric illness, drug/substance abuse, and autoimmune illness. Our dataset consists of 764 phase III cancer trials with these exclusions annotated at the trial level. We experiment with common transformer models as well as a new pre-trained clinical trial BERT model. Our results demonstrate the feasibility of automatically classifying common exclusion criteria. Additionally, we demonstrate the value of a pre-trained language model specifically for clinical trials, which yields the highest average performance across all criteria.  ( 2 min )

Despite the many successful applications of deep learning models for multidimensional signal and image processing, most traditional neural networks process data represented by (multidimensional) arrays of real numbers. The intercorrelation between feature channels is usually expected to be learned from the training data, requiring numerous parameters and careful training. In contrast, vector-valued neural networks are conceived to process arrays of vectors and naturally consider the intercorrelation between feature channels. Consequently, they usually have fewer parameters and often undergo more robust training than traditional neural networks. This paper aims to present a broad framework for vector-valued neural networks, referred to as V-nets. In this context, hypercomplex-valued neural networks are regarded as vector-valued models with additional algebraic properties. Furthermore, this paper explains the relationship between vector-valued and traditional neural networks. Precisely, a vector-valued neural network can be obtained by placing restrictions on a real-valued model to consider the intercorrelation between feature channels. Finally, we show how V-nets, including hypercomplex-valued neural networks, can be implemented in current deep-learning libraries as real-valued networks.  ( 2 min )

The study presents the concept of a computationally efficient machine learning (ML) model for diagnosing and monitoring Parkinson's disease (PD) in an Internet of Things (IoT) environment using rest-state EEG signals (rs-EEG). We computed different types of entropy from EEG signals and found that Fuzzy Entropy performed the best in diagnosing and monitoring PD using rs-EEG. We also investigated different combinations of signal frequency ranges and EEG channels to accurately diagnose PD. Finally, with a fewer number of features (11 features), we achieved a maximum classification accuracy (ARKF) of ~99.9%. The most prominent frequency range of EEG signals has been identified, and we have found that high classification accuracy depends on low-frequency signal components (0-4 Hz). Moreover, the most informative signals were mainly received from the right hemisphere of the head (F8, P8, T8, FC6). Furthermore, we assessed the accuracy of the diagnosis of PD using three different lengths of EEG data (150-1000 samples). Because the computational complexity is reduced by reducing the input data. As a result, we have achieved a maximum mean accuracy of 99.9% for a sample length (LEEG) of 1000 (~7.8 seconds), 98.2% with a LEEG of 800 (~6.2 seconds), and 79.3% for LEEG = 150 (~1.2 seconds). By reducing the number of features and segment lengths, the computational cost of classification can be reduced. Lower-performance smart ML sensors can be used in IoT environments for enhances human resilience to PD.  ( 3 min )

In this work, we provide a characterization of the feature-learning process in two-layer ReLU networks trained by gradient descent on the logistic loss following random initialization. We consider data with binary labels that are generated by an XOR-like function of the input features. We permit a constant fraction of the training labels to be corrupted by an adversary. We show that, although linear classifiers are no better than random guessing for the distribution we consider, two-layer ReLU networks trained by gradient descent achieve generalization error close to the label noise rate. We develop a novel proof technique that shows that at initialization, the vast majority of neurons function as random features that are only weakly correlated with useful features, and the gradient descent dynamics 'amplify' these weak, random features to strong, useful features.  ( 2 min )

The primary goal of this research is to propose a novel architecture for a deep neural network that can solve fractional differential equations accurately. A Gaussian integration rule and a $L_1$ discretization technique are used in the proposed design. In each equation, a deep neural network is used to approximate the unknown function. Three forms of fractional differential equations have been examined to highlight the method's versatility: a fractional ordinary differential equation, a fractional order integrodifferential equation, and a fractional order partial differential equation. The results show that the proposed architecture solves different forms of fractional differential equations with excellent precision.  ( 2 min )

Alzheimer's Disease (AD) is a progressive disease preceded by Mild Cognitive Impairment (MCI). Early detection of AD is crucial for making treatment decisions. However, most of the literature on computer-assisted detection of AD focuses on classifying brain images into one of three major categories: healthy, MCI, and AD; or categorizing MCI patients into (1) progressive: those who progress from MCI to AD at a future examination time, and (2) stable: those who stay as MCI and never progress to AD. This misses the opportunity to accurately identify the trajectory of progressive MCI patients. In this paper, we revisit the brain image classification task for AD identification and re-frame it as an ordinal classification task to predict how close a patient is to the severe AD stage. To this end, we select progressive MCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and construct an ordinal dataset with a prediction target that indicates the time to progression to AD. We train a Siamese network model to predict the time to onset of AD based on MRI brain images. We also propose a Weighted variety of Siamese network and compare its performance to a baseline model. Our evaluations show that incorporating a weighting factor to Siamese networks brings considerable performance gain at predicting how close input brain MRI images are to progressing to AD. Moreover, we complement our results with an interpretation of the learned embedding space of the Siamese networks using a model explainability technique.  ( 3 min )

Reinforcement Learning views the maximization of rewards and avoidance of punishments as central to explaining goal-directed behavior. However, over a life, organisms will need to learn about many different aspects of the world's structure: the states of the world and state-vector transition dynamics. The number of combinations of states grows exponentially as an agent incorporates new knowledge, and there is no obvious weighted combination of pre-existing rewards or costs defined for a given combination of states, as such a weighting would need to encode information about good and bad combinations prior to an agent's experience in the world. Therefore, we must develop more naturalistic accounts of behavior and motivation in large state-spaces. We show that it is possible to use only the intrinsic motivation metric of empowerment, which measures the agent's capacity to realize many possible futures under a transition operator. We propose to scale empowerment to hierarchical state-spaces by using Operator Bellman Equations. These equations produce state-time feasibility functions, which are compositional hierarchical state-time transition operators that map an initial state and time when an agent begins a policy to the final states and times of completing a goal. Because these functions are hierarchical operators we can define hierarchical empowerment measures on them. An agent can then optimize plans to distant states and times to maximize its hierarchical empowerment-gain, allowing it to discover goals that bring about a more favorable coupling of its internal structure (physiological states) to its external environment (world structure & spatial state). Life-long agents could therefore be primarily animated by principles of compositionality and empowerment, exhibiting self-concern for the growth & maintenance of their own structural integrity without recourse to reward-maximization.  ( 3 min )

Electrocardiogram (ECG) is one of the most important diagnostic tools in clinical applications. With the advent of advanced algorithms, various deep learning models have been adopted for ECG tasks. However, the potential of Transformers for ECG data is not yet realized, despite their widespread success in computer vision and natural language processing. In this work, we present a useful masked Transformer method for ECG classification referred to as MTECG, which expands the application of masked autoencoders to ECG time series. We construct a dataset comprising 220,251 ECG recordings with a broad range of diagnoses annoated by medical experts to explore the properties of MTECG. Under the proposed training strategies, a lightweight model with 5.7M parameters performs stably well on a broad range of masking ratios (5%-75%). The ablation studies highlight the importance of fluctuated reconstruction targets, training schedule length, layer-wise LR decay and DropPath rate. The experiments on both private and public ECG datasets demonstrate that MTECG-T significantly outperforms the recent state-of-the-art algorithms in ECG classification.  ( 2 min )

In recent years, Transformer-based auto-attention mechanisms have been successfully applied to the analysis of a variety of context-reliant data types, from texts to images and beyond, including data from non-Euclidean geometries. In this paper, we present such a mechanism, designed to classify sequences of Symmetric Positive Definite matrices while preserving their Riemannian geometry throughout the analysis. We apply our method to automatic sleep staging on timeseries of EEG-derived covariance matrices from a standard dataset, obtaining high levels of stage-wise performance.

We present a novel local-global feature fusion framework for body-weight exercise recognition with floor-based dynamic pressure maps. One step further from the existing studies using deep neural networks mainly focusing on global feature extraction, the proposed framework aims to combine local and global features using image processing techniques and the YOLO object detection to localize pressure profiles from different body parts and consider physical constraints. The proposed local feature extraction method generates two sets of high-level local features consisting of cropped pressure mapping and numerical features such as angular orientation, location on the mat, and pressure area. In addition, we adopt a knowledge distillation for regularization to preserve the knowledge of the global feature extraction and improve the performance of the exercise recognition. Our experimental results demonstrate a notable 11 percent improvement in F1 score for exercise recognition while preserving label-specific features.  ( 2 min )

In Changjun Fan et al. [Nature Communications https://doi.org/10.1038/s41467-023-36363-w (2023)], the authors present a deep reinforced learning approach to augment combinatorial optimization heuristics. In particular, they present results for several spin glass ground state problems, for which instances on non-planar networks are generally NP-hard, in comparison with several Monte Carlo based methods, such as simulated annealing (SA) or parallel tempering (PT). Indeed, those results demonstrate that the reinforced learning improves the results over those obtained with SA or PT, or at least allows for reduced runtimes for the heuristics before results of comparable quality have been obtained relative to those other methods. To facilitate the conclusion that their method is ''superior'', the authors pursue two basic strategies: (1) A commercial GUROBI solver is called on to procure a sample of exact ground states as a testbed to compare with, and (2) a head-to-head comparison between the heuristics is given for a sample of larger instances where exact ground states are hard to ascertain. Here, we put these studies into a larger context, showing that the claimed superiority is at best marginal for smaller samples and becomes essentially irrelevant with respect to any sensible approximation of true ground states in the larger samples. For example, this method becomes irrelevant as a means to determine stiffness exponents $\theta$ in $d>2$, as mentioned by the authors, where the problem is not only NP-hard but requires the subtraction of two almost equal ground-state energies and systemic errors in each of $\approx 1\%$ found here are unacceptable. This larger picture on the method arises from a straightforward finite-size corrections study over the spin glass ensembles the authors employ, using data that has been available for decades.

The marketplace system connecting demands and supplies has been explored to develop unbiased decision-making in valuing properties. Real estate appraisal serves as one of the high-cost property valuation tasks for financial institutions since it requires domain experts to appraise the estimation based on the corresponding knowledge and the judgment of the market. Existing automated valuation models reducing the subjectivity of domain experts require a large number of transactions for effective evaluation, which is predominantly limited to not only the labeling efforts of transactions but also the generalizability of new developing and rural areas. To learn representations from unlabeled real estate sets, existing self-supervised learning (SSL) for tabular data neglects various important features, and fails to incorporate domain knowledge. In this paper, we propose DoRA, a Domain-based self-supervised learning framework for low-resource Real estate Appraisal. DoRA is pre-trained with an intra-sample geographic prediction as the pretext task based on the metadata of the real estate for equipping the real estate representations with prior domain knowledge. Furthermore, inter-sample contrastive learning is employed to generalize the representations to be robust for limited transactions of downstream tasks. Our benchmark results on three property types of real-world transactions show that DoRA significantly outperforms the SSL baselines for tabular data, the graph-based methods, and the supervised approaches in the few-shot scenarios by at least 7.6% for MAPE, 11.59% for MAE, and 3.34% for HR10%. We expect DoRA to be useful to other financial practitioners with similar marketplace applications who need general models for properties that are newly built and have limited records. The source code is available at https://github.com/wwweiwei/DoRA.

Upcoming certification actions related to the security of machine learning (ML) based systems raise major evaluation challenges that are amplified by the large-scale deployment of models in many hardware platforms. Until recently, most of research works focused on API-based attacks that consider a ML model as a pure algorithmic abstraction. However, new implementation-based threats have been revealed, emphasizing the urgency to propose both practical and simulation-based methods to properly evaluate the robustness of models. A major concern is parameter-based attacks (such as the Bit-Flip Attack, BFA) that highlight the lack of robustness of typical deep neural network models when confronted by accurate and optimal alterations of their internal parameters stored in memory. Setting in a security testing purpose, this work practically reports, for the first time, a successful variant of the BFA on a 32-bit Cortex-M microcontroller using laser fault injection. It is a standard fault injection means for security evaluation, that enables to inject spatially and temporally accurate faults. To avoid unrealistic brute-force strategies, we show how simulations help selecting the most sensitive set of bits from the parameters taking into account the laser fault model.

Score-based methods for learning Bayesain networks(BN) aim to maximizing the global score functions. However, if local variables have direct and indirect dependence simultaneously, the global optimization on score functions misses edges between variables with indirect dependent relationship, of which scores are smaller than those with direct dependent relationship. In this paper, we present an identifiability condition based on a determined subset of parents to identify the underlying DAG. By the identifiability condition, we develop a two-phase algorithm namely optimal-tuning (OT) algorithm to locally amend the global optimization. In the optimal phase, an optimization problem based on first-order Hilbert-Schmidt independence criterion (HSIC) gives an estimated skeleton as the initial determined parents subset. In the tuning phase, the skeleton is locally tuned by deletion, addition and DAG-formalization strategies using the theoretically proved incremental properties of high-order HSIC. Numerical experiments for different synthetic datasets and real-world datasets show that the OT algorithm outperforms existing methods. Especially in Sigmoid Mix model with the size of the graph being ${\rm\bf d=40}$, the structure intervention distance (SID) of the OT algorithm is 329.7 smaller than the one obtained by CAM, which indicates that the graph estimated by the OT algorithm misses fewer edges compared with CAM.Source code of the OT algorithm is available at https://github.com/YafeiannWang/optimal-tune-algorithm.

Predicting the dynamics of chaotic systems is one of the most challenging tasks for neural networks, and machine learning in general. Here we aim to predict the spatiotemporal chaotic dynamics of a high-dimensional non-linear system. In our attempt we use the TensorFlow library, representing the state of the art for deep neural networks training and prediction. While our results are encouraging, and show that the dynamics of the considered system can be predicted for short time, we also indirectly discovered an unexpected and undesirable behavior of the TensorFlow library. More specifically, the longer term prediction of the system's chaotic behavior quickly deteriorates and blows up due to the nondeterministic behavior of the TensorFlow library. Here we provide numerical evidence of the short time prediction ability, and of the longer term predictability blow up.

We aim to provide a general framework of for computational photography that recovers the real scene from imperfect images, via the Deep Nonparametric Convexified Filtering (DNCF). It is consists of a nonparametric deep network to resemble the physical equations behind the image formation, such as denoising, super-resolution, inpainting, and flash. DNCF has no parameterization dependent on training data, therefore has a strong generalization and robustness to adversarial image manipulation. During inference, we also encourage the network parameters to be nonnegative and create a bi-convex function on the input and parameters, and this adapts to second-order optimization algorithms with insufficient running time, having 10X acceleration over Deep Image Prior. With these tools, we empirically verify its capability to defend image classification deep networks against adversary attack algorithms in real-time.

Apache TVM (Tensor Virtual Machine), an open source machine learning compiler framework designed to optimize computations across various hardware platforms, provides an opportunity to improve the performance of dense matrix factorizations such as LU (Lower Upper) decomposition and Cholesky decomposition on GPUs and AI (Artificial Intelligence) accelerators. In this paper, we propose a new TVM autotuning framework using Bayesian Optimization and use the TVM tensor expression language to implement linear algebra kernels such as LU, Cholesky, and 3mm. We use these scientific computation kernels to evaluate the effectiveness of our methods on a GPU cluster, called Swing, at Argonne National Laboratory. We compare the proposed autotuning framework with the TVM autotuning framework AutoTVM with four tuners and find that our framework outperforms AutoTVM in most cases.

Focus in Explainable AI is shifting from explanations defined in terms of low-level elements, such as input features, to explanations encoded in terms of interpretable concepts learned from data. How to reliably acquire such concepts is, however, still fundamentally unclear. An agreed-upon notion of concept interpretability is missing, with the result that concepts used by both post-hoc explainers and concept-based neural networks are acquired through a variety of mutually incompatible strategies. Critically, most of these neglect the human side of the problem: a representation is understandable only insofar as it can be understood by the human at the receiving end. The key challenge in Human-interpretable Representation Learning (HRL) is how to model and operationalize this human element. In this work, we propose a mathematical framework for acquiring interpretable representations suitable for both post-hoc explainers and concept-based neural networks. Our formalization of HRL builds on recent advances in causal representation learning and explicitly models a human stakeholder as an external observer. This allows us to derive a principled notion of alignment between the machine representation and the vocabulary of concepts understood by the human. In doing so, we link alignment and interpretability through a simple and intuitive name transfer game, and clarify the relationship between alignment and a well-known property of representations, namely disentanglment. We also show that alignment is linked to the issue of undesirable correlations among concepts, also known as concept leakage, and to content-style separation, all through a general information-theoretic reformulation of these properties. Our conceptualization aims to bridge the gap between the human and algorithmic sides of interpretability and establish a stepping stone for new research on human-interpretable representations.

We introduce a machine-learning framework to warm-start fixed-point optimization algorithms. Our architecture consists of a neural network mapping problem parameters to warm starts, followed by a predefined number of fixed-point iterations. We propose two loss functions designed to either minimize the fixed-point residual or the distance to a ground truth solution. In this way, the neural network predicts warm starts with the end-to-end goal of minimizing the downstream loss. An important feature of our architecture is its flexibility, in that it can predict a warm start for fixed-point algorithms run for any number of steps, without being limited to the number of steps it has been trained on. We provide PAC-Bayes generalization bounds on unseen data for common classes of fixed-point operators: contractive, linearly convergent, and averaged. Applying this framework to well-known applications in control, statistics, and signal processing, we observe a significant reduction in the number of iterations and solution time required to solve these problems, through learned warm starts.

We consider nonconvex-concave minimax problems, $\min_{\mathbf{x}} \max_{\mathbf{y} \in \mathcal{Y}} f(\mathbf{x}, \mathbf{y})$, where $f$ is nonconvex in $\mathbf{x}$ but concave in $\mathbf{y}$ and $\mathcal{Y}$ is a convex and bounded set. One of the most popular algorithms for solving this problem is the celebrated gradient descent ascent (GDA) algorithm, which has been widely used in machine learning, control theory and economics. Despite the extensive convergence results for the convex-concave setting, GDA with equal stepsize can converge to limit cycles or even diverge in a general setting. In this paper, we present the complexity results on two-time-scale GDA for solving nonconvex-concave minimax problems, showing that the algorithm can find a stationary point of the function $\Phi(\cdot) := \max_{\mathbf{y} \in \mathcal{Y}} f(\cdot, \mathbf{y})$ efficiently. To the best our knowledge, this is the first nonasymptotic analysis for two-time-scale GDA in this setting, shedding light on its superior practical performance in training generative adversarial networks (GANs) and other real applications.

Artificial intelligence models and methods commonly lack causal interpretability. Despite the advancements in interpretable machine learning (IML) methods, they frequently assign importance to features which lack causal influence on the outcome variable. Selecting causally relevant features among those identified as relevant by these methods, or even before model training, would offer a solution. Feature selection methods utilizing information theoretical quantities have been successful in identifying statistically relevant features. However, the information theoretical quantities they are based on do not incorporate causality, rendering them unsuitable for such scenarios. To address this challenge, this article proposes information theoretical quantities that incorporate the causal structure of the system, which can be used to evaluate causal importance of features for some given outcome variable. Specifically, we introduce causal versions of entropy and mutual information, termed causal entropy and causal information gain, which are designed to assess how much control a feature provides over the outcome variable. These newly defined quantities capture changes in the entropy of a variable resulting from interventions on other variables. Fundamental results connecting these quantities to the existence of causal effects are derived. The use of causal information gain in feature selection is demonstrated, highlighting its superiority over standard mutual information in revealing which features provide control over a chosen outcome variable. Our investigation paves the way for the development of methods with improved interpretability in domains involving causation.

Current physics-informed (standard or operator) neural networks still rely on accurately learning the initial conditions of the system they are solving. In contrast, standard numerical methods evolve such initial conditions without needing to learn these. In this study, we propose to improve current physics-informed deep learning strategies such that initial conditions do not need to be learned and are represented exactly in the predicted solution. Moreover, this method guarantees that when a DeepONet is applied multiple times to time step a solution, the resulting function is continuous.

Deep learning was recently successfully used in deriving symmetry transformations that preserve important physics quantities. Being completely agnostic, these techniques postpone the identification of the discovered symmetries to a later stage. In this letter we propose methods for examining and identifying the group-theoretic structure of such machine-learned symmetries. We design loss functions which probe the subalgebra structure either during the deep learning stage of symmetry discovery or in a subsequent post-processing stage. We illustrate the new methods with examples from the U(n) Lie group family, obtaining the respective subalgebra decompositions. As an application to particle physics, we demonstrate the identification of the residual symmetries after the spontaneous breaking of non-Abelian gauge symmetries like SU(3) and SU(5) which are commonly used in model building.

Auditory spatial attention detection (ASAD) aims to decode the attended spatial location with EEG in a multiple-speaker setting. ASAD methods are inspired by the brain lateralization of cortical neural responses during the processing of auditory spatial attention, and show promising performance for the task of auditory attention decoding (AAD) with neural recordings. In the previous ASAD methods, the spatial distribution of EEG electrodes is not fully exploited, which may limit the performance of these methods. In the present work, by transforming the original EEG channels into a two-dimensional (2D) spatial topological map, the EEG data is transformed into a three-dimensional (3D) arrangement containing spatial-temporal information. And then a 3D deep convolutional neural network (DenseNet-3D) is used to extract temporal and spatial features of the neural representation for the attended locations. The results show that the proposed method achieves higher decoding accuracy than the state-of-the-art (SOTA) method (94.4% compared to XANet's 90.6%) with 1-second decision window for the widely used KULeuven (KUL) dataset, and the code to implement our work is available on Github: https://github.com/xuxiran/ASAD_DenseNet

Adversarial robustness, which primarily comprises sensitivity-based robustness and spatial robustness, plays an integral part in achieving robust generalization. In this paper, we endeavor to design strategies to achieve universal adversarial robustness. To achieve this, we first investigate the relatively less-explored realm of spatial robustness. Then, we integrate the existing spatial robustness methods by incorporating both local and global spatial vulnerability into a unified spatial attack and adversarial training approach. Furthermore, we present a comprehensive relationship between natural accuracy, sensitivity-based robustness, and spatial robustness, supported by strong evidence from the perspective of robust representation. Crucially, to reconcile the interplay between the mutual impacts of various robustness components into one unified framework, we incorporate the \textit{Pareto criterion} into the adversarial robustness analysis, yielding a novel strategy called Pareto Adversarial Training for achieving universal robustness. The resulting Pareto front, which delineates the set of optimal solutions, provides an optimal balance between natural accuracy and various adversarial robustness. This sheds light on solutions for achieving universal robustness in the future. To the best of our knowledge, we are the first to consider universal adversarial robustness via multi-objective optimization.

Open-ended learning benefits immensely from the use of symbolic methods for goal representation as they offer ways to structure knowledge for efficient and transferable learning. However, the existing Hierarchical Reinforcement Learning (HRL) approaches relying on symbolic reasoning are often limited as they require a manual goal representation. The challenge in autonomously discovering a symbolic goal representation is that it must preserve critical information, such as the environment dynamics. In this paper, we propose a developmental mechanism for goal discovery via an emergent representation that abstracts (i.e., groups together) sets of environment states that have similar roles in the task. We introduce a Feudal HRL algorithm that concurrently learns both the goal representation and a hierarchical policy. The algorithm uses symbolic reachability analysis for neural networks to approximate the transition relation among sets of states and to refine the goal representation. We evaluate our approach on complex navigation tasks, showing the learned representation is interpretable, transferrable and results in data efficient learning.

Label scarcity in a graph is frequently encountered in real-world applications due to the high cost of data labeling. To this end, semi-supervised domain adaptation (SSDA) on graphs aims to leverage the knowledge of a labeled source graph to aid in node classification on a target graph with limited labels. SSDA tasks need to overcome the domain gap between the source and target graphs. However, to date, this challenging research problem has yet to be formally considered by the existing approaches designed for cross-graph node classification. To tackle the SSDA problem on graphs, a novel method called SemiGCL is proposed, which benefits from graph contrastive learning and minimax entropy training. SemiGCL generates informative node representations by contrasting the representations learned from a graph's local and global views. Additionally, SemiGCL is adversarially optimized with the entropy loss of unlabeled target nodes to reduce domain divergence. Experimental results on benchmark datasets demonstrate that SemiGCL outperforms the state-of-the-art baselines on the SSDA tasks.

We introduce beta diffusion, a novel generative modeling method that integrates demasking and denoising to generate data within bounded ranges. Using scaled and shifted beta distributions, beta diffusion utilizes multiplicative transitions over time to create both forward and reverse diffusion processes, maintaining beta distributions in both the forward marginals and the reverse conditionals, given the data at any point in time. Unlike traditional diffusion-based generative models relying on additive Gaussian noise and reweighted evidence lower bounds (ELBOs), beta diffusion is multiplicative and optimized with KL-divergence upper bounds (KLUBs) derived from the convexity of the KL divergence. We demonstrate that the proposed KLUBs are more effective for optimizing beta diffusion compared to negative ELBOs, which can also be derived as the KLUBs of the same KL divergence with its two arguments swapped. The loss function of beta diffusion, expressed in terms of Bregman divergence, further supports the efficacy of KLUBs for optimization. Experimental results on both synthetic data and natural images demonstrate the unique capabilities of beta diffusion in generative modeling of range-bounded data and validate the effectiveness of KLUBs in optimizing diffusion models, thereby making them valuable additions to the family of diffusion-based generative models and the optimization techniques used to train them.

UCI WorldTour races, the premier men's elite road cycling tour, are grueling events that put physical fitness and endurance of riders to the test. The coaches of Team Jumbo-Visma have long been responsible for predicting the energy needs of each rider of the Dutch team for every race on the calendar. Those must be estimated to ensure riders have the energy and resources necessary to maintain a high level of performance throughout a race. This task, however, is both time-consuming and challenging, as it requires precise estimates of race speed and power output. Traditionally, the approach to predicting energy needs has relied on judgement and experience of coaches, but this method has its limitations and often leads to inaccurate predictions. In this paper, we propose a new, more effective approach to predicting energy needs for cycling races. By predicting the speed and power with regression models, we provide the coaches with calorie needs estimates for each individual rider per stage instantly. In addition, we compare methods to quantify uncertainty using conformal prediction. The empirical analysis of the jackknife+, jackknife-minmax, jackknife-minmax-after-bootstrap, CV+, CV-minmax, conformalized quantile regression, and inductive conformal prediction methods in conformal prediction reveals that all methods achieve valid prediction intervals. All but minmax-based methods also produce sufficiently narrow prediction intervals for decision-making. Furthermore, methods computing prediction intervals of fixed size produce tighter intervals for low significance values. Among the methods computing intervals of varying length across the input space, inductive conformal prediction computes narrower prediction intervals at larger significance level.

In this paper, we consider the intersection of two problems in machine learning: Multi-Source Domain Adaptation (MSDA) and Dataset Distillation (DD). On the one hand, the first considers adapting multiple heterogeneous labeled source domains to an unlabeled target domain. On the other hand, the second attacks the problem of synthesizing a small summary containing all the information about the datasets. We thus consider a new problem called MSDA-DD. To solve it, we adapt previous works in the MSDA literature, such as Wasserstein Barycenter Transport and Dataset Dictionary Learning, as well as DD method Distribution Matching. We thoroughly experiment with this novel problem on four benchmarks (Caltech-Office 10, Tennessee-Eastman Process, Continuous Stirred Tank Reactor, and Case Western Reserve University), where we show that, even with as little as 1 sample per class, one achieves state-of-the-art adaptation performance.

Comparing graphs by means of optimal transport has recently gained significant attention, as the distances induced by optimal transport provide both a principled metric between graphs as well as an interpretable description of the associated changes between graphs in terms of a transport plan. As the lack of symmetry introduces challenges in the typically considered formulations, optimal transport distances for graphs have mostly been developed for undirected graphs. Here, we propose two distance measures to compare directed graphs based on variants of optimal transport: (i) an earth movers distance (Wasserstein) and (ii) a Gromov-Wasserstein (GW) distance. We evaluate these two distances and discuss their relative performance for both simulated graph data and real-world directed cell-cell communication graphs, inferred from single-cell RNA-seq data.

Purpose: Common to most MRSI techniques, the spatial resolution and the minimal scan duration of Deuterium Metabolic Imaging (DMI) are limited by the achievable SNR. This work presents a deep learning method for sensitivity enhancement of DMI. Methods: A convolutional neural network (CNN) was designed to estimate the 2H-labeled metabolite concentrations from low SNR and distorted DMI FIDs. The CNN was trained with synthetic data that represent a range of SNR levels typically encountered in vivo. The estimation precision was further improved by fine-tuning the CNN with MRI-based edge-preserving regularization for each DMI dataset. The proposed processing method, PReserved Edge ConvolutIonal neural network for Sensitivity Enhanced DMI (PRECISE-DMI), was applied to simulation studies and in vivo experiments to evaluate the anticipated improvements in SNR and investigate the potential for inaccuracies. Results: PRECISE-DMI visually improved the metabolic maps of low SNR datasets, and quantitatively provided higher precision than the standard Fourier reconstruction. Processing of DMI data acquired in rat brain tumor models resulted in more precise determination of 2H-labeled lactate and glutamate + glutamine levels, at increased spatial resolution (from >8 to 2 $\mu$L) or shortened scan time (from 32 to 4 min) compared to standard acquisitions. However, rigorous SD-bias analyses showed that overuse of the edge-preserving regularization can compromise the accuracy of the results. Conclusion: PRECISE-DMI allows a flexible trade-off between enhancing the sensitivity of DMI and minimizing the inaccuracies. With typical settings, the DMI sensitivity can be improved by 3-fold while retaining the capability to detect local signal variations.

Given a set of points labeled with $k$ labels, we introduce the heat map sorting problem as reordering and merging the points and dimensions while preserving the clusters (labels). A cluster is preserved if it remains connected, i.e., if it is not split into several clusters and no two clusters are merged. We prove the problem is NP-hard and we give a fixed-parameter algorithm with a constant number of rounds in the massively parallel computation model, where each machine has a sublinear memory and the total memory of the machines is linear. We give an approximation algorithm for a NP-hard special case of the problem. We empirically compare our algorithm with k-means and density-based clustering (DBSCAN) using a dimensionality reduction via locality-sensitive hashing on several directed and undirected graphs of email and computer networks.

During image editing, existing deep generative models tend to re-synthesize the entire output from scratch, including the unedited regions. This leads to a significant waste of computation, especially for minor editing operations. In this work, we present Spatially Sparse Inference (SSI), a general-purpose technique that selectively performs computation for edited regions and accelerates various generative models, including both conditional GANs and diffusion models. Our key observation is that users prone to gradually edit the input image. This motivates us to cache and reuse the feature maps of the original image. Given an edited image, we sparsely apply the convolutional filters to the edited regions while reusing the cached features for the unedited areas. Based on our algorithm, we further propose Sparse Incremental Generative Engine (SIGE) to convert the computation reduction to latency reduction on off-the-shelf hardware. With about $1\%$-area edits, SIGE accelerates DDPM by $3.0\times$ on NVIDIA RTX 3090 and $4.6\times$ on Apple M1 Pro GPU, Stable Diffusion by $7.2\times$ on 3090, and GauGAN by $5.6\times$ on 3090 and $5.2\times$ on M1 Pro GPU. Compared to our conference version, we extend SIGE to accommodate attention layers and apply it to Stable Diffusion. Additionally, we offer support for Apple M1 Pro GPU and include more results with large and sequential edits.

The goal of universal audio representation learning is to obtain foundational models that can be used for a variety of downstream tasks involving speech, music or environmental sounds. To approach this problem, methods inspired by self-supervised models from NLP, like BERT, are often used and adapted to audio. These models rely on the discrete nature of text, hence adopting this type of approach for audio processing requires either a change in the learning objective or mapping the audio signal to a set of discrete classes. In this work, we explore the use of EnCodec, a neural audio codec, to generate discrete targets for learning an universal audio model based on a masked autoencoder (MAE). We evaluate this approach, which we call EncodecMAE, on a wide range of audio tasks spanning speech, music and environmental sounds, achieving performances comparable or better than leading audio representation models.

Benign overfitting, the phenomenon where interpolating models generalize well in the presence of noisy data, was first observed in neural network models trained with gradient descent. To better understand this empirical observation, we consider the generalization error of two-layer neural networks trained to interpolation by gradient descent on the logistic loss following random initialization. We assume the data comes from well-separated class-conditional log-concave distributions and allow for a constant fraction of the training labels to be corrupted by an adversary. We show that in this setting, neural networks exhibit benign overfitting: they can be driven to zero training error, perfectly fitting any noisy training labels, and simultaneously achieve minimax optimal test error. In contrast to previous work on benign overfitting that require linear or kernel-based predictors, our analysis holds in a setting where both the model and learning dynamics are fundamentally nonlinear.

Tens of thousands of simultaneous hypothesis tests are routinely performed in genomic studies to identify differentially expressed genes. However, due to unmeasured confounders, many standard statistical approaches may be substantially biased. This paper investigates the large-scale hypothesis testing problem for multivariate generalized linear models in the presence of confounding effects. Under arbitrary confounding mechanisms, we propose a unified statistical estimation and inference framework that harnesses orthogonal structures and integrates linear projections into three key stages. It first leverages multivariate responses to separate marginal and uncorrelated confounding effects, recovering the confounding coefficients' column space. Subsequently, latent factors and primary effects are jointly estimated, utilizing $\ell_1$-regularization for sparsity while imposing orthogonality onto confounding coefficients. Finally, we incorporate projected and weighted bias-correction steps for hypothesis testing. Theoretically, we establish various effects' identification conditions and non-asymptotic error bounds. We show effective Type-I error control of asymptotic $z$-tests as sample and response sizes approach infinity. Numerical experiments demonstrate that the proposed method controls the false discovery rate by the Benjamini-Hochberg procedure and is more powerful than alternative methods. By comparing single-cell RNA-seq counts from two groups of samples, we demonstrate the suitability of adjusting confounding effects when significant covariates are absent from the model.

Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment. Although EEG-based AAD methods have shown promising results in recent years, current approaches primarily rely on traditional convolutional neural network designed for processing Euclidean data like images. This makes it challenging to handle EEG signals, which possess non-Euclidean characteristics. In order to address this problem, this paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input. Specifically, to effectively represent the non-Euclidean properties of EEG signals, dynamical graph convolutional networks are applied to represent the graph structure of EEG signals, which can also extract crucial features related to auditory spatial attention in EEG signals. In addition, to further improve AAD detection performance, self-distillation, consisting of feature distillation and hierarchical distillation strategies at each layer, is integrated. These strategies leverage features and classification results from the deepest network layers to guide the learning of shallow layers. Our experiments are conducted on two publicly available datasets, KUL and DTU. Under a 1-second time window, we achieve results of 90.0\% and 79.6\% accuracy on KUL and DTU, respectively. We compare our DGSD method with competitive baselines, and the experimental results indicate that the detection performance of our proposed DGSD method is not only superior to the best reproducible baseline but also significantly reduces the number of trainable parameters by approximately 100 times.

This article summarizes a systematic review of the electroencephalography (EEG)-based cognitive workload (CWL) estimation. The focus of the article is twofold: identify the disparate experimental paradigms used for reliably eliciting discreet and quantifiable levels of cognitive load and the specific nature and representational structure of the commonly used input formulations in deep neural networks (DNNs) used for signal classification. The analysis revealed a number of studies using EEG signals in its native representation of a two-dimensional matrix for offline classification of CWL. However, only a few studies adopted an online or pseudo-online classification strategy for real-time CWL estimation. Further, only a couple of interpretable DNNs and a single generative model were employed for cognitive load detection till date during this review. More often than not, researchers were using DNNs as black-box type models. In conclusion, DNNs prove to be valuable tools for classifying EEG signals, primarily due to the substantial modeling power provided by the depth of their network architecture. It is further suggested that interpretable and explainable DNN models must be employed for cognitive workload estimation since existing methods are limited in the face of the non-stationary nature of the signal.

Pushing the boundaries of machine learning often requires exploring different hardware and software combinations. However, the freedom to experiment across different tooling stacks can be at odds with the drive for efficiency, which has produced increasingly specialized AI hardware and incentivized consolidation around a narrow set of ML frameworks. Exploratory research can be restricted if software and hardware are co-evolving, making it even harder to stray away from mainstream ideas that work well with popular tooling stacks. While this friction increasingly impacts the rate of innovation in machine learning, to our knowledge the lack of portability in tooling has not been quantified. In this work, we ask: How portable are popular ML software frameworks? We conduct a large-scale study of the portability of mainstream ML frameworks across different hardware types. Our findings paint an uncomfortable picture -- frameworks can lose more than 40% of their key functions when ported to other hardware. Worse, even when functions are portable, the slowdown in their performance can be extreme and render performance untenable. Collectively, our results reveal how costly straying from a narrow set of hardware-software combinations can be - and suggest that specialization of hardware impedes innovation in machine learning research.

Machine Learning (ML) has emerged as a promising approach in healthcare, outperforming traditional statistical techniques. However, to establish ML as a reliable tool in clinical practice, adherence to best practices regarding data handling, experimental design, and model evaluation is crucial. This work summarizes and strictly observes such practices to ensure reproducible and reliable ML. Specifically, we focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. We investigate the impact of different data augmentation techniques and model complexity on the overall performance. We consider MRI data from ADNI dataset to address a classification problem employing 3D Convolutional Neural Network (CNN). The experiments are designed to compensate for data scarcity and initial random parameters by utilizing cross-validation and multiple training trials. Within this framework, we train 15 predictive models, considering three different data augmentation strategies and five distinct 3D CNN architectures, each varying in the number of convolutional layers. Specifically, the augmentation strategies are based on affine transformations, such as zoom, shift, and rotation, applied concurrently or separately. The combined effect of data augmentation and model complexity leads to a variation in prediction performance up to 10% of accuracy. When affine transformation are applied separately, the model is more accurate, independently from the adopted architecture. For all strategies, the model accuracy followed a concave behavior at increasing number of convolutional layers, peaking at an intermediate value of layers. The best model (8 CL, (B)) is the most stable across cross-validation folds and training trials, reaching excellent performance both on the testing set and on an external test set.

In this paper, we exploit the unique properties of a deterministic projected belief network (D-PBN) to take full advantage of trainable compound activation functions (TCAs). A D-PBN is a type of auto-encoder that operates by "backing up" through a feed-forward neural network. TCAs are activation functions with complex monotonic-increasing shapes that change the distribution of the data so that the linear transformation that follows is more effective. Because a D-PBN operates by "backing up", the TCAs are inverted in the reconstruction process, restoring the original distribution of the data, thus taking advantage of a given TCA in both analysis and reconstruction. In this paper, we show that a D-PBN auto-encoder with TCAs can significantly out-perform standard auto-encoders including variational auto-encoders.

Variable importance assessment has become a crucial step in machine-learning applications when using complex learners, such as deep neural networks, on large-scale data. Removal-based importance assessment is currently the reference approach, particularly when statistical guarantees are sought to justify variable inclusion. It is often implemented with variable permutation schemes. On the flip side, these approaches risk misidentifying unimportant variables as important in the presence of correlations among covariates. Here we develop a systematic approach for studying Conditional Permutation Importance (CPI) that is model agnostic and computationally lean, as well as reusable benchmarks of state-of-the-art variable importance estimators. We show theoretically and empirically that $\textit{CPI}$ overcomes the limitations of standard permutation importance by providing accurate type-I error control. When used with a deep neural network, $\textit{CPI}$ consistently showed top accuracy across benchmarks. An empirical benchmark on real-world data analysis in a large-scale medical dataset showed that $\textit{CPI}$ provides a more parsimonious selection of statistically significant variables. Our results suggest that $\textit{CPI}$ can be readily used as drop-in replacement for permutation-based methods.

Computational pathology uses artificial intelligence to enable precision medicine and decision support systems through the analysis of whole slide images. It has the potential to revolutionize the diagnosis and treatment of cancer. However, a major challenge to this objective is that for many specific computational pathology tasks the amount of data is inadequate for development. To address this challenge, we created Virchow, a 632 million parameter deep neural network foundation model for computational pathology. Using self-supervised learning, Virchow is trained on 1.5 million hematoxylin and eosin stained whole slide images from diverse tissue groups, which is orders of magnitude more data than previous works. When evaluated on downstream tasks including tile-level pan-cancer detection and subtyping and slide-level biomarker prediction, Virchow outperforms state-of-the-art systems both on internal datasets drawn from the same population as the pretraining data as well as external public datasets. Virchow achieves 93% balanced accuracy for pancancer tile classification, and AUCs of 0.983 for colon microsatellite instability status prediction and 0.967 for breast CDH1 status prediction. The gains in performance highlight the importance of pretraining on massive pathology image datasets, suggesting pretraining on even larger datasets could continue improving performance for many high-impact applications where limited amounts of training data are available, such as drug outcome prediction.

Large pre-trained vision-language models such as CLIP have demonstrated great potential in zero-shot transferability to downstream tasks. However, to attain optimal performance, the manual selection of prompts is necessary to improve alignment between the downstream image distribution and the textual class descriptions. This manual prompt engineering is the major challenge for deploying such models in practice since it requires domain expertise and is extremely time-consuming. To avoid non-trivial prompt engineering, recent work Context Optimization (CoOp) introduced the concept of prompt learning to the vision domain using learnable textual tokens. While CoOp can achieve substantial improvements over manual prompts, its learned context is worse generalizable to wider unseen classes within the same dataset. In this work, we present Prompt Learning with Reparameterization Encoder (PRE) - a simple and efficient method that enhances the generalization ability of the learnable prompt to unseen classes while maintaining the capacity to learn Base classes. Instead of directly optimizing the prompts, PRE employs a prompt encoder to reparameterize the input prompt embeddings, enhancing the exploration of task-specific knowledge from few-shot samples. Experiments and extensive ablation studies on 8 benchmarks demonstrate that our approach is an efficient method for prompt learning. Specifically, PRE achieves a notable enhancement of 5.60% in average accuracy on New classes and 3% in Harmonic mean compared to CoOp in the 16-shot setting, all achieved within a good training time.

In recent studies, linear recurrent neural networks (LRNNs) have achieved Transformer-level performance in natural language modeling and long-range modeling while offering rapid parallel training and constant inference costs. With the resurged interest in LRNNs, we study whether they can learn the hidden rules in training sequences, such as the grammatical structures of regular language. We theoretically analyze some existing LRNNs and discover their limitations on regular language. Motivated by the analysis, we propose a new LRNN equipped with a block-diagonal and input-dependent transition matrix. Experiments suggest that the proposed model is the only LRNN that can perform length extrapolation on regular language tasks such as Sum, Even Pair, and Modular Arithmetic.

By identifying similarities between successive inputs, Self-Supervised Learning (SSL) methods for time series analysis have demonstrated their effectiveness in encoding the inherent static characteristics of temporal data. However, an exclusive emphasis on similarities might result in representations that overlook the dynamic attributes critical for modeling cardiovascular diseases within a confined subject cohort. Introducing Distilled Encoding Beyond Similarities (DEBS), this paper pioneers an SSL approach that transcends mere similarities by integrating dissimilarities among positive pairs. The framework is applied to electrocardiogram (ECG) signals, leading to a notable enhancement of +10\% in the detection accuracy of Atrial Fibrillation (AFib) across diverse subjects. DEBS underscores the potential of attaining a more refined representation by encoding the dynamic characteristics of time series data, tapping into dissimilarities during the optimization process. Broadly, the strategy delineated in this study holds the promise of unearthing novel avenues for advancing SSL methodologies tailored to temporal data.

Text-to-image diffusion models have achieved widespread popularity due to their unprecedented image generation capability. In particular, their ability to synthesize and modify human faces has spurred research into using generated face images in both training data augmentation and model performance assessments. In this paper, we study the efficacy and shortcomings of generative models in the context of face generation. Utilizing a combination of qualitative and quantitative measures, including embedding-based metrics and user studies, we present a framework to audit the characteristics of generated faces conditioned on a set of social attributes. We applied our framework on faces generated through state-of-the-art text-to-image diffusion models. We identify several limitations of face image generation that include faithfulness to the text prompt, demographic disparities, and distributional shifts. Furthermore, we present an analytical model that provides insights into how training data selection contributes to the performance of generative models.

The myriad complex systems with multiway interactions motivate the extension of graph-based pairwise connections to higher-order relations. In particular, the simplicial complex has inspired generalizations of graph neural networks (GNNs) to simplicial complex-based models. Learning on such systems requires large amounts of data, which can be expensive or impossible to obtain. We propose data augmentation of simplicial complexes through both linear and nonlinear mixup mechanisms that return mixtures of existing labeled samples. In addition to traditional pairwise mixup, we present a convex clustering mixup approach for a data-driven relationship among several simplicial complexes. We theoretically demonstrate that the resultant synthetic simplicial complexes interpolate among existing data with respect to homomorphism densities. Our method is demonstrated on both synthetic and real-world datasets for simplicial complex classification.

The main premise of federated learning (FL) is that machine learning model updates are computed locally to preserve user data privacy. This approach avoids by design user data to ever leave the perimeter of their device. Once the updates aggregated, the model is broadcast to all nodes in the federation. However, without proper defenses, compromised nodes can probe the model inside their local memory in search for adversarial examples, which can lead to dangerous real-world scenarios. For instance, in image-based applications, adversarial examples consist of images slightly perturbed to the human eye getting misclassified by the local model. These adversarial images are then later presented to a victim node's counterpart model to replay the attack. Typical examples harness dissemination strategies such as altered traffic signs (patch attacks) no longer recognized by autonomous vehicles or seemingly unaltered samples that poison the local dataset of the FL scheme to undermine its robustness. Pelta is a novel shielding mechanism leveraging Trusted Execution Environments (TEEs) that reduce the ability of attackers to craft adversarial samples. Pelta masks inside the TEE the first part of the back-propagation chain rule, typically exploited by attackers to craft the malicious samples. We evaluate Pelta on state-of-the-art accurate models using three well-established datasets: CIFAR-10, CIFAR-100 and ImageNet. We show the effectiveness of Pelta in mitigating six white-box state-of-the-art adversarial attacks, such as Projected Gradient Descent, Momentum Iterative Method, Auto Projected Gradient Descent, the Carlini & Wagner attack. In particular, Pelta constitutes the first attempt at defending an ensemble model against the Self-Attention Gradient attack to the best of our knowledge. Our code is available to the research community at https://github.com/queyrusi/Pelta.

This work investigates the applicability of recent generative Large Language Models (LLMs), such as the GPT series and Flan-T5, to ontology alignment for identifying concept equivalence mappings across ontologies. To test the zero-shot performance of Flan-T5-XXL and GPT-3.5-turbo, we leverage challenging subsets from two equivalence matching datasets of the OAEI Bio-ML track, taking into account concept labels and structural contexts. Preliminary findings suggest that LLMs have the potential to outperform existing ontology alignment systems like BERTMap, given careful framework and prompt design.

Topological signal processing (TSP) utilizes simplicial complexes to model structures with higher order than vertices and edges. In this paper, we study the transferability of TSP via a generalized higher-order version of graphon, known as complexon. We recall the notion of a complexon as the limit of a simplicial complex sequence [1]. Inspired by the integral operator form of graphon shift operators, we construct a marginal complexon and complexon shift operator (CSO) according to components of all possible dimensions from the complexon. We investigate the CSO's eigenvalues and eigenvectors, and relate them to a new family of weighted adjacency matrices. We prove that when a simplicial complex sequence converges to a complexon, the eigenvalues of the corresponding CSOs converge to that of the limit complexon. These results hint at learning transferability on large simplicial complexes or simplicial complex sequences, which generalize the graphon signal processing framework.

In global healthcare, respiratory diseases are a leading cause of mortality, underscoring the need for rapid and accurate diagnostics. To advance rapid screening techniques via auscultation, our research focuses on employing one of the largest publicly available medical database of respiratory sounds to train multiple machine learning models able to classify different health conditions. Our method combines Empirical Mode Decomposition (EMD) and spectral analysis to extract physiologically relevant biosignals from acoustic data, closely tied to cardiovascular and respiratory patterns, making our approach apart in its departure from conventional audio feature extraction practices. We use Power Spectral Density analysis and filtering techniques to select Intrinsic Mode Functions (IMFs) strongly correlated with underlying physiological phenomena. These biosignals undergo a comprehensive feature extraction process for predictive modeling. Initially, we deploy a binary classification model that demonstrates a balanced accuracy of 87% in distinguishing between healthy and diseased individuals. Subsequently, we employ a six-class classification model that achieves a balanced accuracy of 72% in diagnosing specific respiratory conditions like pneumonia and chronic obstructive pulmonary disease (COPD). For the first time, we also introduce regression models that estimate age and body mass index (BMI) based solely on acoustic data, as well as a model for gender classification. Our findings underscore the potential of this approach to significantly enhance assistive and remote diagnostic capabilities.

Unsupervised contrastive learning methods have recently seen significant improvements, particularly through data augmentation strategies that aim to produce robust and generalizable representations. However, prevailing data augmentation methods, whether hand designed or based on foundation models, tend to rely heavily on prior knowledge or external data. This dependence often compromises their effectiveness and efficiency. Furthermore, the applicability of most existing data augmentation strategies is limited when transitioning to other research domains, especially science-related data. This limitation stems from the paucity of prior knowledge and labeled data available in these domains. To address these challenges, we introduce DiffAug-a novel and efficient Diffusion-based data Augmentation technique. DiffAug aims to ensure that the augmented and original data share a smoothed latent space, which is achieved through diffusion steps. Uniquely, unlike traditional methods, DiffAug first mines sufficient prior semantic knowledge about the neighborhood. This provides a constraint to guide the diffusion steps, eliminating the need for labels, external data/models, or prior knowledge. Designed as an architecture-agnostic framework, DiffAug provides consistent improvements. Specifically, it improves image classification and clustering accuracy by 1.6%~4.5%. When applied to biological data, DiffAug improves performance by up to 10.1%, with an average improvement of 5.8%. DiffAug shows good performance in both vision and biological domains.

Line outage identification in distribution grids is essential for sustainable grid operation. In this work, we propose a practical yet robust detection approach that utilizes only readily available voltage magnitudes, eliminating the need for costly phase angles or power flow data. Given the sensor data, many existing detection methods based on change-point detection require prior knowledge of outage patterns, which are unknown for real-world outage scenarios. To remove this impractical requirement, we propose a data-driven method to learn the parameters of the post-outage distribution through gradient descent. However, directly using gradient descent presents feasibility issues. To address this, we modify our approach by adding a Bregman divergence constraint to control the trajectory of the parameter updates, which eliminates the feasibility problems. As timely operation is the key nowadays, we prove that the optimal parameters can be learned with convergence guarantees via leveraging the statistical and physical properties of voltage data. We evaluate our approach using many representative distribution grids and real load profiles with 17 outage configurations. The results show that we can detect and localize the outage in a timely manner with only voltage magnitudes and without assuming a prior knowledge of outage patterns.

Scientific Machine Learning (SciML) is a burgeoning field that synergistically combines domain-aware and interpretable models with agnostic machine learning techniques. In this work, we introduce GOKU-UI, an evolution of the SciML generative model GOKU-nets. GOKU-UI not only broadens the original model's spectrum to incorporate other classes of differential equations, such as Stochastic Differential Equations (SDEs), but also integrates attention mechanisms and a novel multiple shooting training strategy in the latent space. These modifications have led to a significant increase in its performance in both reconstruction and forecast tasks, as demonstrated by our evaluation of simulated and empirical data. Specifically, GOKU-UI outperformed all baseline models on synthetic datasets even with a training set 16-fold smaller, underscoring its remarkable data efficiency. Furthermore, when applied to empirical human brain data, while incorporating stochastic Stuart-Landau oscillators into its dynamical core, our proposed enhancements markedly increased the model's effectiveness in capturing complex brain dynamics. This augmented version not only surpassed all baseline methods in the reconstruction task, but also demonstrated lower prediction error of future brain activity up to 15 seconds ahead. By training GOKU-UI on resting state fMRI data, we encoded whole-brain dynamics into a latent representation, learning a low-dimensional dynamical system model that could offer insights into brain functionality and open avenues for practical applications such as the classification of mental states or psychiatric conditions. Ultimately, our research provides further impetus for the field of Scientific Machine Learning, showcasing the potential for advancements when established scientific insights are interwoven with modern machine learning.

We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI. Our main motivation is to propose a very simple baseline to compare to, using only very standard ingredients from the literature. We evaluate its performance on four EEG Motor Imagery datasets, including simulated online setups, and compare it to recent Deep Learning and Machine Learning approaches. EEG-SimpleConv is at least as good or far more efficient than other approaches, showing strong knowledge-transfer capabilities across subjects, at the cost of a low inference time. We advocate that using off-the-shelf ingredients rather than coming with ad-hoc solutions can significantly help the adoption of Deep Learning approaches for BCI. We make the code of the models and the experiments accessible.

Many complex real world phenomena exhibit abrupt, intermittent or jumping behaviors, which are more suitable to be described by stochastic differential equations under non-Gaussian L\'evy noise. Among these complex phenomena, the most likely transition paths between metastable states are important since these rare events may have a high impact in certain scenarios. Based on the large deviation principle, the most likely transition path could be treated as the minimizer of the rate function upon paths that connect two points. One of the challenges to calculate the most likely transition path for stochastic dynamical systems under non-Gaussian L\'evy noise is that the associated rate function can not be explicitly expressed by paths. For this reason, we formulate an optimal control problem to obtain the optimal state as the most likely transition path. We then develop a neural network method to solve this issue. Several experiments are investigated for both Gaussian and non-Gaussian cases.

Decoding visual representations from human brain activity has emerged as a thriving research domain, particularly in the context of brain-computer interfaces. Our study presents an innovative method that employs to classify and reconstruct images from the ImageNet dataset using electroencephalography (EEG) data from subjects that had viewed the images themselves (i.e. "brain decoding"). We analyzed EEG recordings from 6 participants, each exposed to 50 images spanning 40 unique semantic categories. These EEG readings were converted into spectrograms, which were then used to train a convolutional neural network (CNN), integrated with a knowledge distillation procedure based on a pre-trained Contrastive Language-Image Pre-Training (CLIP)-based image classification teacher network. This strategy allowed our model to attain a top-5 accuracy of 80%, significantly outperforming a standard CNN and various RNN-based benchmarks. Additionally, we incorporated an image reconstruction mechanism based on pre-trained latent diffusion models, which allowed us to generate an estimate of the images which had elicited EEG activity. Therefore, our architecture not only decodes images from neural activity but also offers a credible image reconstruction from EEG only, paving the way for e.g. swift, individualized feedback experiments. Our research represents a significant step forward in connecting neural signals with visual cognition.

The integration of manipulator robots in household environments suggests a need for more predictable and human-like robot motion. This holds especially true for wheelchair-mounted assistive robots that can support the independence of people with paralysis. One method of generating naturalistic motion trajectories is via the imitation of human demonstrators. This paper explores a self-supervised imitation learning method using an autoregressive spatio-temporal graph neural network for an assistive drinking task. We address learning from diverse human motion trajectory data that were captured via wearable IMU sensors on a human arm as the action-free task demonstrations. Observed arm motion data from several participants is used to generate natural and functional drinking motion trajectories for a UR5e robot arm.

Quantile Regression (QR) can be used to estimate aleatoric uncertainty in deep neural networks and can generate prediction intervals. Quantifying uncertainty is particularly important in critical applications such as clinical diagnosis, where a realistic assessment of uncertainty is essential in determining disease status and planning the appropriate treatment. The most common application of quantile regression models is in cases where the parametric likelihood cannot be specified. Although quantile regression is quite robust to outlier response observations, it can be sensitive to outlier covariate observations (features). Outlier features can compromise the performance of deep learning regression problems such as style translation, image reconstruction, and deep anomaly detection, potentially leading to misleading conclusions. To address this problem, we propose a robust solution for quantile regression that incorporates concepts from robust divergence. We compare the performance of our proposed method with (i) least trimmed quantile regression and (ii) robust regression based on the regularization of case-specific parameters in a simple real dataset in the presence of outlier. These methods have not been applied in a deep learning framework. We also demonstrate the applicability of the proposed method by applying it to a medical imaging translation task using diffusion models.

Human activity recognition (HAR) is an essential research field that has been used in different applications including home and workplace automation, security and surveillance as well as healthcare. Starting from conventional machine learning methods to the recently developing deep learning techniques and the Internet of things, significant contributions have been shown in the HAR area in the last decade. Even though several review and survey studies have been published, there is a lack of sensor-based HAR overview studies focusing on summarising the usage of wearable sensors and smart home sensors data as well as applications of HAR and deep learning techniques. Hence, we overview sensor-based HAR, discuss several important applications that rely on HAR, and highlight the most common machine learning methods that have been used for HAR. Finally, several challenges of HAR are explored that should be addressed to further improve the robustness of HAR.

A major challenge in imaging genetics and similar fields is to link high-dimensional data in one domain, e.g., genetic data, to high dimensional data in a second domain, e.g., brain imaging data. The standard approach in the area are mass univariate analyses across genetic factors and imaging phenotypes. That entails executing one genome-wide association study (GWAS) for each pre-defined imaging measure. Although this approach has been tremendously successful, one shortcoming is that phenotypes must be pre-defined. Consequently, effects that are not confined to pre-selected regions of interest or that reflect larger brain-wide patterns can easily be missed. In this work we introduce a Partial Least Squares (PLS)-based framework, which we term Cluster-Bootstrap PLS (CLUB-PLS), that can work with large input dimensions in both domains as well as with large sample sizes. One key factor of the framework is to use cluster bootstrap to provide robust statistics for single input features in both domains. We applied CLUB-PLS to investigating the genetic basis of surface area and cortical thickness in a sample of 33,000 subjects from the UK Biobank. We found 107 genome-wide significant locus-phenotype pairs that are linked to 386 different genes. We found that a vast majority of these loci could be technically validated at a high rate: using classic GWAS or Genome-Wide Inferred Statistics (GWIS) we found that 85 locus-phenotype pairs exceeded the genome-wide suggestive (P<1e-05) threshold.

One of the common human diseases is sleep disorders. The classification of sleep stages plays a fundamental role in diagnosing sleep disorders, monitoring treatment effectiveness, and understanding the relationship between sleep stages and various health conditions. A precise and efficient classification of these stages can significantly enhance our understanding of sleep-related phenomena and ultimately lead to improved health outcomes and disease treatment. Models others propose are often time-consuming and lack sufficient accuracy, especially in stage N1. The main objective of this research is to present a machine-learning model called "EEGMobile". This model utilizes pre-trained models and learns from electroencephalogram (EEG) spectrograms of brain signals. The model achieved an accuracy of 86.97% on a publicly available dataset named "Sleep-EDF20", outperforming other models proposed by different researchers. Moreover, it recorded an accuracy of 56.4% in stage N1, which is better than other models. These findings demonstrate that this model has the potential to achieve better results for the treatment of this disease.

Quantum computing is a growing field where the information is processed by two-levels quantum states known as qubits. Current physical realizations of qubits require a careful calibration, composed by different experiments, due to noise and decoherence phenomena. Among the different characterization experiments, a crucial step is to develop a model to classify the measured state by discriminating the ground state from the excited state. In this proceedings we benchmark multiple classification techniques applied to real quantum devices.

With the rapid development of electronic science and technology, the research on wearable devices is constantly updated, but for now, it is not comprehensive for wearable devices to recognize and analyze the movement of specific sports. Based on this, this paper improves wearable devices of table tennis sport, and realizes the pattern recognition and evaluation of table tennis players' motor skills through artificial intelligence. Firstly, a device is designed to collect the movement information of table tennis players and the actual movement data is processed. Secondly, a sliding window is made to divide the collected motion data into a characteristic database of six table tennis benchmark movements. Thirdly, motion features were constructed based on feature engineering, and motor skills were identified for different models after dimensionality reduction. Finally, the hierarchical evaluation system of motor skills is established with the loss functions of different evaluation indexes. The results show that in the recognition of table tennis players' motor skills, the feature-based BP neural network proposed in this paper has higher recognition accuracy and stronger generalization ability than the traditional convolutional neural network.

Maximizing influences in complex networks is a practically important but computationally challenging task for social network analysis, due to its NP- hard nature. Most current approximation or heuristic methods either require tremendous human design efforts or achieve unsatisfying balances between effectiveness and efficiency. Recent machine learning attempts only focus on speed but lack performance enhancement. In this paper, different from previous attempts, we propose an effective deep reinforcement learning model that achieves superior performances over traditional best influence maximization algorithms. Specifically, we design an end-to-end learning framework that combines graph neural network as the encoder and reinforcement learning as the decoder, named DREIM. Trough extensive training on small synthetic graphs, DREIM outperforms the state-of-the-art baseline methods on very large synthetic and real-world networks on solution quality, and we also empirically show its linear scalability with regard to the network size, which demonstrates its superiority in solving this problem.

Sleep, a fundamental physiological process, occupies a significant portion of our lives. Accurate classification of sleep stages serves as a crucial tool for evaluating sleep quality and identifying probable sleep disorders. This work introduces a novel methodology that utilises a SE-Resnet-Bi-LSTM architecture to classify sleep into five separate stages. The classification process is based on the analysis of single-channel electroencephalograms (EEGs). The framework that has been suggested consists of two fundamental elements: a feature extractor that utilises SE-ResNet, and a temporal context encoder that use stacks of Bi-LSTM units.The effectiveness of our approach is substantiated by thorough assessments conducted on three different datasets, namely SLeepEDF-20, SleepEDF-78, and SHHS. Significantly, our methodology attains notable levels of accuracy, specifically 87.5\%, 83.9\%, and 87.8\%, along with macro-F1 scores of 82.5, 78.9, and 81.9 for the corresponding datasets. Notably, we introduce the utilization of 1D-GradCAM visualization to shed light on the decision-making process of our model in the realm of sleep stage classification. This visualization method not only provides valuable insights into the model's classification rationale but also aligns its outcomes with the annotations made by sleep experts. One notable feature of our research is the integration of an expedited training approach, which effectively preserves the model's resilience in terms of performance. The experimental evaluations conducted provide a comprehensive evaluation of the effectiveness of our proposed model in comparison to existing approaches, highlighting its potential for practical applications.

This paper proposes a direct text to speech translation system using discrete acoustic units. This framework employs text in different source languages as input to generate speech in the target language without the need for text transcriptions in this language. Motivated by the success of acoustic units in previous works for direct speech to speech translation systems, we use the same pipeline to extract the acoustic units using a speech encoder combined with a clustering algorithm. Once units are obtained, an encoder-decoder architecture is trained to predict them. Then a vocoder generates speech from units. Our approach for direct text to speech translation was tested on the new CVSS corpus with two different text mBART models employed as initialisation. The systems presented report competitive performance for most of the language pairs evaluated. Besides, results show a remarkable improvement when initialising our proposed architecture with a model pre-trained with more languages.

Artistic authoring of 3D environments is a laborious enterprise that also requires skilled content creators. There have been impressive improvements in using machine learning to address different aspects of generating 3D content, such as generating meshes, arranging geometry, synthesizing textures, etc. In this paper we develop a model to generate Bidirectional Reflectance Distribution Functions (BRDFs) from descriptive textual prompts. BRDFs are four dimensional probability distributions that characterize the interaction of light with surface materials. They are either represented parametrically, or by tabulating the probability density associated with every pair of incident and outgoing angles. The former lends itself to artistic editing while the latter is used when measuring the appearance of real materials. Numerous works have focused on hypothesizing BRDF models from images of materials. We learn a mapping from textual descriptions of materials to parametric BRDFs. Our model is first trained using a semi-supervised approach before being tuned via an unsupervised scheme. Although our model is general, in this paper we specifically generate parameters for MDL materials, conditioned on natural language descriptions, within NVIDIA's Omniverse platform. This enables use cases such as real-time text prompts to change materials of objects in 3D environments such as "dull plastic" or "shiny iron". Since the output of our model is a parametric BRDF, rather than an image of the material, it may be used to render materials using any shape under arbitrarily specified viewing and lighting conditions.

In data-rich domains such as vision, language, and speech, deep learning prevails to deliver high-performance task-specific models and can even learn general task-agnostic representations for efficient finetuning to downstream tasks. However, deep learning in resource-limited domains still faces multiple challenges including (i) limited data, (ii) constrained model development cost, and (iii) lack of adequate pre-trained models for effective finetuning. This paper provides an overview of model reprogramming to bridge this gap. Model reprogramming enables resource-efficient cross-domain machine learning by repurposing and reusing a well-developed pre-trained model from a source domain to solve tasks in a target domain without model finetuning, where the source and target domains can be vastly different. In many applications, model reprogramming outperforms transfer learning and training from scratch. This paper elucidates the methodology of model reprogramming, summarizes existing use cases, provides a theoretical explanation of the success of model reprogramming, and concludes with a discussion on open-ended research questions and opportunities. A list of model reprogramming studies is actively maintained and updated at https://github.com/IBM/model-reprogramming.

The discovery of governing equations from data has been an active field of research for decades. One widely used methodology for this purpose is sparse regression for nonlinear dynamics, known as SINDy. Despite several attempts, noisy and scarce data still pose a severe challenge to the success of the SINDy approach. In this work, we discuss a robust method to discover nonlinear governing equations from noisy and scarce data. To do this, we make use of neural networks to learn an implicit representation based on measurement data so that not only it produces the output in the vicinity of the measurements but also the time-evolution of output can be described by a dynamical system. Additionally, we learn such a dynamic system in the spirit of the SINDy framework. Leveraging the implicit representation using neural networks, we obtain the derivative information -- required for SINDy -- using an automatic differentiation tool. To enhance the robustness of our methodology, we further incorporate an integral condition on the output of the implicit networks. Furthermore, we extend our methodology to handle data collected from multiple initial conditions. We demonstrate the efficiency of the proposed methodology to discover governing equations under noisy and scarce data regimes by means of several examples and compare its performance with existing methods.

Spiking neural networks (SNNs) process time-series data via internal event-driven neural dynamics whose energy consumption depends on the number of spikes exchanged between neurons over the course of the input presentation. In typical implementations of an SNN classifier, decisions are produced after the entire input sequence has been processed, resulting in latency and energy consumption levels that are fairly uniform across inputs. Recently introduced delay-adaptive SNNs tailor the inference latency -- and, with it, the energy consumption -- to the difficulty of each example, by producing an early decision when the SNN model is sufficiently ``confident''. In this paper, we start by observing that, as an SNN processes input samples, its classification decisions tend to be first under-confident and then over-confident with respect to the decision's ground-truth, unknown, test accuracy. This makes it difficult to determine a stopping time that ensures a desired level of accuracy. To address this problem, we introduce a novel delay-adaptive SNN-based inference methodology that, wrapping around any pre-trained SNN classifier, provides guaranteed reliability for the decisions produced at input-dependent stopping times. The approach entails minimal added complexity as compared to the underlying SNN, requiring only thresholding and counting operations at run time, and it leverages tools from conformal prediction (CP).

While the recently introduced Tree of Thoughts (ToT) has heralded advancements in allowing Large Language Models (LLMs) to reason through foresight and backtracking for global decision-making, it has overlooked the inherent local uncertainties in intermediate decision points or "thoughts". These local uncertainties, intrinsic to LLMs given their potential for diverse responses, remain a significant concern in the reasoning process. Addressing this pivotal gap, we introduce the Tree of Uncertain Thoughts (TouT) - a reasoning framework tailored for LLMs. Our TouT effectively leverages Monte Carlo Dropout to quantify uncertainty scores associated with LLMs' diverse local responses at these intermediate steps. By marrying this local uncertainty quantification with global search algorithms, TouT enhances the model's precision in response generation. We substantiate our approach with rigorous experiments on two demanding planning tasks: Game of 24 and Mini Crosswords. The empirical evidence underscores TouT's superiority over both ToT and chain-of-thought prompting methods.

We present a novel approach to address the challenge of generalization in offline reinforcement learning (RL), where the agent learns from a fixed dataset without any additional interaction with the environment. Specifically, we aim to improve the agent's ability to generalize to out-of-distribution goals. To achieve this, we propose to learn a dynamics model and check if it is equivariant with respect to a fixed type of transformation, namely translations in the state space. We then use an entropy regularizer to increase the equivariant set and augment the dataset with the resulting transformed samples. Finally, we learn a new policy offline based on the augmented dataset, with an off-the-shelf offline RL algorithm. Our experimental results demonstrate that our approach can greatly improve the test performance of the policy on the considered environments.

Adversarial defenses protect machine learning models from adversarial attacks, but are often tailored to one type of model or attack. The lack of information on unknown potential attacks makes detecting adversarial examples challenging. Additionally, attackers do not need to follow the rules made by the defender. To address this problem, we take inspiration from the concept of Applicability Domain in cheminformatics. Cheminformatics models struggle to make accurate predictions because only a limited number of compounds are known and available for training. Applicability Domain defines a domain based on the known compounds and rejects any unknown compound that falls outside the domain. Similarly, adversarial examples start as harmless inputs, but can be manipulated to evade reliable classification by moving outside the domain of the classifier. We are the first to identify the similarity between Applicability Domain and adversarial detection. Instead of focusing on unknown attacks, we focus on what is known, the training data. We propose a simple yet robust triple-stage data-driven framework that checks the input globally and locally, and confirms that they are coherent with the model's output. This framework can be applied to any classification model and is not limited to specific attacks. We demonstrate these three stages work as one unit, effectively detecting various attacks, even for a white-box scenario.

In the presence of heterogeneous data, where randomly rotated objects fall into multiple underlying categories, it is challenging to simultaneously classify them into clusters and synchronize them based on pairwise relations. This gives rise to the joint problem of community detection and synchronization. We propose a series of semidefinite relaxations, and prove their exact recovery when extending the celebrated stochastic block model to this new setting where both rotations and cluster identities are to be determined. Numerical experiments demonstrate the efficacy of our proposed algorithms and confirm our theoretical result which indicates a sharp phase transition for exact recovery.

Languages are not created randomly but rather to communicate information. There is a strong association between languages and their underlying meanings, resulting in a sparse joint distribution that is heavily peaked according to their correlations. Moreover, these peak values happen to match with the marginal distribution of languages due to the sparsity. With the advent of LLMs trained on big data and large models, we can now precisely assess the marginal distribution of languages, providing a convenient means of exploring the sparse structures in the joint distribution for effective inferences. In this paper, we categorize languages as either unambiguous or {\epsilon}-ambiguous and present quantitative results to demonstrate that the emergent abilities of LLMs, such as language understanding, in-context learning, chain-of-thought prompting, and effective instruction fine-tuning, can all be attributed to Bayesian inference on the sparse joint distribution of languages.

In this article, we propose an approach for federated domain adaptation, a setting where distributional shift exists among clients and some have unlabeled data. The proposed framework, FedDaDiL, tackles the resulting challenge through dictionary learning of empirical distributions. In our setting, clients' distributions represent particular domains, and FedDaDiL collectively trains a federated dictionary of empirical distributions. In particular, we build upon the Dataset Dictionary Learning framework by designing collaborative communication protocols and aggregation operations. The chosen protocols keep clients' data private, thus enhancing overall privacy compared to its centralized counterpart. We empirically demonstrate that our approach successfully generates labeled data on the target domain with extensive experiments on (i) Caltech-Office, (ii) TEP, and (iii) CWRU benchmarks. Furthermore, we compare our method to its centralized counterpart and other benchmarks in federated domain adaptation.

Several fundamental problems in science and engineering consist of global optimization tasks involving unknown high-dimensional (black-box) functions that map a set of controllable variables to the outcomes of an expensive experiment. Bayesian Optimization (BO) techniques are known to be effective in tackling global optimization problems using a relatively small number objective function evaluations, but their performance suffers when dealing with high-dimensional outputs. To overcome the major challenge of dimensionality, here we propose a deep learning framework for BO and sequential decision making based on bootstrapped ensembles of neural architectures with randomized priors. Using appropriate architecture choices, we show that the proposed framework can approximate functional relationships between design variables and quantities of interest, even in cases where the latter take values in high-dimensional vector spaces or even infinite-dimensional function spaces. In the context of BO, we augmented the proposed probabilistic surrogates with re-parameterized Monte Carlo approximations of multiple-point (parallel) acquisition functions, as well as methodological extensions for accommodating black-box constraints and multi-fidelity information sources. We test the proposed framework against state-of-the-art methods for BO and demonstrate superior performance across several challenging tasks with high-dimensional outputs, including a constrained multi-fidelity optimization task involving shape optimization of rotor blades in turbo-machinery.

We show that unless P=NP, there cannot be a polynomial-time algorithm that finds a point within Euclidean distance $c^n$ (for any constant $c \ge 0$) of a local minimizer of an $n$-variate quadratic function over a polytope. This result (even with $c=0$) answers a question of Pardalos and Vavasis that appeared in 1992 on a list of seven open problems in complexity theory for numerical optimization. Our proof technique also implies that the problem of deciding whether a quadratic function has a local minimizer over an (unbounded) polyhedron, and that of deciding if a quartic polynomial has a local minimizer are NP-hard.

Unveiling the underlying governing equations of nonlinear dynamic systems remains a significant challenge, especially when encountering noisy observations and no prior knowledge available. This study proposes R-DISCOVER, a framework designed to robustly uncover open-form partial differential equations (PDEs) from limited and noisy data. The framework operates through two alternating update processes: discovering and embedding. The discovering phase employs symbolic representation and a reinforcement learning (RL)-guided hybrid PDE generator to efficiently produce diverse open-form PDEs with tree structures. A neural network-based predictive model fits the system response and serves as the reward evaluator for the generated PDEs. PDEs with superior fits are utilized to iteratively optimize the generator via the RL method and the best-performing PDE is selected by a parameter-free stability metric. The embedding phase integrates the initially identified PDE from the discovering process as a physical constraint into the predictive model for robust training. The traversal of PDE trees automates the construction of the computational graph and the embedding process without human intervention. Numerical experiments demonstrate our framework's capability to uncover governing equations from nonlinear dynamic systems with limited and highly noisy data and outperform other physics-informed neural network-based discovery methods. This work opens new potential for exploring real-world systems with limited understanding.

Vision Transformers (ViTs) have demonstrated the state-of-the-art performance in various vision-related tasks. The success of ViTs motivates adversaries to perform backdoor attacks on ViTs. Although the vulnerability of traditional CNNs to backdoor attacks is well-known, backdoor attacks on ViTs are seldom-studied. Compared to CNNs capturing pixel-wise local features by convolutions, ViTs extract global context information through patches and attentions. Na\"ively transplanting CNN-specific backdoor attacks to ViTs yields only a low clean data accuracy and a low attack success rate. In this paper, we propose a stealth and practical ViT-specific backdoor attack $TrojViT$. Rather than an area-wise trigger used by CNN-specific backdoor attacks, TrojViT generates a patch-wise trigger designed to build a Trojan composed of some vulnerable bits on the parameters of a ViT stored in DRAM memory through patch salience ranking and attention-target loss. TrojViT further uses minimum-tuned parameter update to reduce the bit number of the Trojan. Once the attacker inserts the Trojan into the ViT model by flipping the vulnerable bits, the ViT model still produces normal inference accuracy with benign inputs. But when the attacker embeds a trigger into an input, the ViT model is forced to classify the input to a predefined target class. We show that flipping only few vulnerable bits identified by TrojViT on a ViT model using the well-known RowHammer can transform the model into a backdoored one. We perform extensive experiments of multiple datasets on various ViT models. TrojViT can classify $99.64\%$ of test images to a target class by flipping $345$ bits on a ViT for ImageNet.Our codes are available at https://github.com/mxzheng/TrojViT

The open radio access network (O-RAN) architecture supports intelligent network control algorithms as one of its core capabilities. Data-driven applications incorporate such algorithms to optimize radio access network (RAN) functions via RAN intelligent controllers (RICs). Deep reinforcement learning (DRL) algorithms are among the main approaches adopted in the O-RAN literature to solve dynamic radio resource management problems. However, despite the benefits introduced by the O-RAN RICs, the practical adoption of DRL algorithms in real network deployments falls behind. This is primarily due to the slow convergence and unstable performance exhibited by DRL agents upon deployment and when facing previously unseen network conditions. In this paper, we address these challenges by proposing transfer learning (TL) as a core component of the training and deployment workflows for the DRL-based closed-loop control of O-RAN functionalities. To this end, we propose and design a hybrid TL-aided approach that leverages the advantages of both policy reuse and distillation TL methods to provide safe and accelerated convergence in DRL-based O-RAN slicing. We conduct a thorough experiment that accommodates multiple services, including real VR gaming traffic to reflect practical scenarios of O-RAN slicing. We also propose and implement policy reuse and distillation-aided DRL and non-TL-aided DRL as three separate baselines. The proposed hybrid approach shows at least: 7.7% and 20.7% improvements in the average initial reward value and the percentage of converged scenarios, and a 64.6% decrease in reward variance while maintaining fast convergence and enhancing the generalizability compared with the baselines.

The task of blind source separation (BSS) involves separating sources from a mixture without prior knowledge of the sources or the mixing system. This is a challenging problem that often requires making restrictive assumptions about both the mixing system and the sources. In this paper, we propose a novel method for addressing BSS of non-linear mixtures by leveraging the natural feature subspace specialization ability of multi-encoder autoencoders with fully self-supervised learning without strong priors. During the training phase, our method unmixes the input into the separate encoding spaces of the multi-encoder network and then remixes these representations within the decoder for a reconstruction of the input. Then to perform source inference, we introduce a novel encoding masking technique whereby masking out all but one of the encodings enables the decoder to estimate a source signal. To this end, we also introduce a so-called pathway separation loss that encourages sparsity between the unmixed encoding spaces throughout the decoder's layers and a so-called zero reconstruction loss on the decoder for coherent source estimations. In order to carefully evaluate our method, we conduct experiments on a toy dataset and with real-world biosignal recordings from a polysomnography sleep study for extracting respiration.

In the Variational Autoencoder (VAE), the variational posterior often aligns closely with the prior, which is known as posterior collapse and hinders the quality of representation learning. To mitigate this problem, an adjustable hyperparameter beta has been introduced in the VAE. This paper presents a closed-form expression to assess the relationship between the beta in VAE, the dataset size, the posterior collapse, and the rate-distortion curve by analyzing a minimal VAE in a high-dimensional limit. These results clarify that a long plateau in the generalization error emerges with a relatively larger beta. As the beta increases, the length of the plateau extends and then becomes infinite beyond a certain beta threshold. This implies that the choice of beta, unlike the usual regularization parameters, can induce posterior collapse regardless of the dataset size. Thus, beta is a risky parameter that requires careful tuning. Furthermore, considering the dataset-size dependence on the rate-distortion curve, a relatively large dataset is required to obtain a rate-distortion curve with high rates. Extensive numerical experiments support our analysis.

Quantum reinforcement learning (QRL) has emerged as a framework to solve sequential decision-making tasks, showcasing empirical quantum advantages. A notable development is through quantum recurrent neural networks (QRNNs) for memory-intensive tasks such as partially observable environments. However, QRL models incorporating QRNN encounter challenges such as inefficient training of QRL with QRNN, given that the computation of gradients in QRNN is both computationally expensive and time-consuming. This work presents a novel approach to address this challenge by constructing QRL agents utilizing QRNN-based reservoirs, specifically employing quantum long short-term memory (QLSTM). QLSTM parameters are randomly initialized and fixed without training. The model is trained using the asynchronous advantage actor-aritic (A3C) algorithm. Through numerical simulations, we validate the efficacy of our QLSTM-Reservoir RL framework. Its performance is assessed on standard benchmarks, demonstrating comparable results to a fully trained QLSTM RL model with identical architecture and training settings.

We develop a theoretical framework for $S_n$-equivariant convolutional quantum circuits with SU$(d)$-symmetry, building on and significantly generalizing Jordan's Permutational Quantum Computing (PQC) formalism based on Schur-Weyl duality connecting both SU$(d)$ and $S_n$ actions on qudits. In particular, we utilize the Okounkov-Vershik approach to prove Harrow's statement (Ph.D. Thesis 2005 p.160) on the equivalence between $\operatorname{SU}(d)$ and $S_n$ irrep bases and to establish the $S_n$-equivariant Convolutional Quantum Alternating Ans\"atze ($S_n$-CQA) using Young-Jucys-Murphy (YJM) elements. We prove that $S_n$-CQA is able to generate any unitary in any given $S_n$ irrep sector, which may serve as a universal model for a wide array of quantum machine learning problems with the presence of SU($d$) symmetry. Our method provides another way to prove the universality of Quantum Approximate Optimization Algorithm (QAOA) and verifies that 4-local SU($d$) symmetric unitaries are sufficient to build generic SU($d$) symmetric quantum circuits up to relative phase factors. We present numerical simulations to showcase the effectiveness of the ans\"atze to find the ground state energy of the $J_1$--$J_2$ antiferromagnetic Heisenberg model on the rectangular and Kagome lattices. Our work provides the first application of the celebrated Okounkov-Vershik's $S_n$ representation theory to quantum physics and machine learning, from which to propose quantum variational ans\"atze that strongly suggests to be classically intractable tailored towards a specific optimization problem.

This paper aims at the algorithmic/theoretical core of reinforcement learning (RL) by introducing the novel class of proximal Bellman mappings. These mappings are defined in reproducing kernel Hilbert spaces (RKHSs), to benefit from the rich approximation properties and inner product of RKHSs, they are shown to belong to the powerful Hilbertian family of (firmly) nonexpansive mappings, regardless of the values of their discount factors, and possess ample degrees of design freedom to even reproduce attributes of the classical Bellman mappings and to pave the way for novel RL designs. An approximate policy-iteration scheme is built on the proposed class of mappings to solve the problem of selecting online, at every time instance, the "optimal" exponent $p$ in a $p$-norm loss to combat outliers in linear adaptive filtering, without training data and any knowledge on the statistical properties of the outliers. Numerical tests on synthetic data showcase the superior performance of the proposed framework over several non-RL and kernel-based RL schemes.

Accelerating the learning of Partial Differential Equations (PDEs) from experimental data will speed up the pace of scientific discovery. Previous randomized algorithms exploit sparsity in PDE updates for acceleration. However such methods are applicable to a limited class of decomposable PDEs, which have sparse features in the value domain. We propose Reel, which accelerates the learning of PDEs via random projection and has much broader applicability. Reel exploits the sparsity by decomposing dense updates into sparse ones in both the value and frequency domains. This decomposition enables efficient learning when the source of the updates consists of gradually changing terms across large areas (sparse in the frequency domain) in addition to a few rapid updates concentrated in a small set of "interfacial" regions (sparse in the value domain). Random projection is then applied to compress the sparse signals for learning. To expand the model applicability, Taylor series expansion is used in Reel to approximate the nonlinear PDE updates with polynomials in the decomposable form. Theoretically, we derive a constant factor approximation between the projected loss function and the original one with poly-logarithmic number of projected dimensions. Experimentally, we provide empirical evidence that our proposed Reel can lead to faster learning of PDE models (70-98% reduction in training time when the data is compressed to 1% of its original size) with comparable quality as the non-compressed models.

A rising trend in theoretical deep learning is to understand why deep learning works through Neural Tangent Kernel (NTK) [jgh18], a kernel method that is equivalent to using gradient descent to train a multi-layer infinitely-wide neural network. NTK is a major step forward in the theoretical deep learning because it allows researchers to use traditional mathematical tools to analyze properties of deep neural networks and to explain various neural network techniques from a theoretical view. A natural extension of NTK on graph learning is \textit{Graph Neural Tangent Kernel (GNTK)}, and researchers have already provide GNTK formulation for graph-level regression and show empirically that this kernel method can achieve similar accuracy as GNNs on various bioinformatics datasets [dhs+19]. The remaining question now is whether solving GNTK regression is equivalent to training an infinite-wide multi-layer GNN using gradient descent. In this paper, we provide three new theoretical results. First, we formally prove this equivalence for graph-level regression. Second, we present the first GNTK formulation for node-level regression. Finally, we prove the equivalence for node-level regression.

This paper studies convergence rates for some value function approximations that arise in a collection of reproducing kernel Hilbert spaces (RKHS) $H(\Omega)$. By casting an optimal control problem in a specific class of native spaces, strong rates of convergence are derived for the operator equation that enables offline approximations that appear in policy iteration. Explicit upper bounds on error in value function approximations are derived in terms of power function $\Pwr_{H,N}$ for the space of finite dimensional approximants $H_N$ in the native space $H(\Omega)$. These bounds are geometric in nature and refine some well-known, now classical results concerning convergence of approximations of value functions.

This paper investigates a challenging problem of zero-shot learning in the multi-label scenario (MLZSL), wherein, the model is trained to recognize multiple unseen classes within a sample (e.g., an image) based on seen classes and auxiliary knowledge, e.g., semantic information. Existing methods usually resort to analyzing the relationship of various seen classes residing in a sample from the dimension of spatial or semantic characteristics, and transfer the learned model to unseen ones. But they ignore the effective integration of local and global features. That is, in the process of inferring unseen classes, global features represent the principal direction of the image in the feature space, while local features should maintain uniqueness within a certain range. This integrated neglect will make the model lose its grasp of the main components of the image. Relying only on the local existence of seen classes during the inference stage introduces unavoidable bias. In this paper, we propose a novel and effective group bi-enhancement framework for MLZSL, dubbed GBE-MLZSL, to fully make use of such properties and enable a more accurate and robust visual-semantic projection. Specifically, we split the feature maps into several feature groups, of which each feature group can be trained independently with the Local Information Distinguishing Module (LID) to ensure uniqueness. Meanwhile, a Global Enhancement Module (GEM) is designed to preserve the principal direction. Besides, a static graph structure is designed to construct the correlation of local features. Experiments on large-scale MLZSL benchmark datasets NUS-WIDE and Open-Images-v4 demonstrate that the proposed GBE-MLZSL outperforms other state-of-the-art methods with large margins.

In the domain of cardiovascular healthcare, the Electrocardiogram (ECG) serves as a critical, non-invasive diagnostic tool. Although recent strides in self-supervised learning (SSL) have been promising for ECG representation learning, these techniques often require annotated samples and struggle with classes not present in the fine-tuning stages. To address these limitations, we introduce ECG-Text Pre-training (ETP), an innovative framework designed to learn cross-modal representations that link ECG signals with textual reports. For the first time, this framework leverages the zero-shot classification task in the ECG domain. ETP employs an ECG encoder along with a pre-trained language model to align ECG signals with their corresponding textual reports. The proposed framework excels in both linear evaluation and zero-shot classification tasks, as demonstrated on the PTB-XL and CPSC2018 datasets, showcasing its ability for robust and generalizable cross-modal ECG feature learning.

Partial differential equations (PDEs) are used to describe a variety of physical phenomena. Often these equations do not have analytical solutions and numerical approximations are used instead. One of the common methods to solve PDEs is the finite element method. Computing derivative information of the solution with respect to the input parameters is important in many tasks in scientific computing. We extend JAX automatic differentiation library with an interface to Firedrake finite element library. High-level symbolic representation of PDEs allows bypassing differentiating through low-level possibly many iterations of the underlying nonlinear solvers. Differentiating through Firedrake solvers is done using tangent-linear and adjoint equations. This enables the efficient composition of finite element solvers with arbitrary differentiable programs. The code is available at github.com/IvanYashchuk/jax-firedrake.

We propose Gibbs-Duhem-informed neural networks for the prediction of binary activity coefficients at varying compositions. That is, we include the Gibbs-Duhem equation explicitly in the loss function for training neural networks, which is straightforward in standard machine learning (ML) frameworks enabling automatic differentiation. In contrast to recent hybrid ML approaches, our approach does not rely on embedding a specific thermodynamic model inside the neural network and corresponding prediction limitations. Rather, Gibbs-Duhem consistency serves as regularization, with the flexibility of ML models being preserved. Our results show increased thermodynamic consistency and generalization capabilities for activity coefficient predictions by Gibbs-Duhem-informed graph neural networks and matrix completion methods. We also find that the model architecture, particularly the activation function, can have a strong influence on the prediction quality. The approach can be easily extended to account for other thermodynamic consistency conditions.

MELAGE, a pioneering Python-based neuroimaging software, emerges as a versatile tool for the visualization, processing, and analysis of medical images. Initially conceived to address the unique challenges of processing 3D ultrasound and MRI brain images during the neonatal period, MELAGE exhibits remarkable adaptability, extending its utility to the domain of adult human brain imaging. At its core, MELAGE features a semi-automatic brain extraction tool empowered by a deep learning module, ensuring precise and efficient brain structure extraction from MRI and 3D Ultrasound data. Moreover, MELAGE offers a comprehensive suite of features, encompassing dynamic 3D visualization, accurate measurements, and interactive image segmentation. This transformative software holds immense promise for researchers and clinicians, offering streamlined image analysis, seamless integration with deep learning algorithms, and broad applicability in the realm of medical imaging.

Embodied AI is a crucial frontier in robotics, capable of planning and executing action sequences for robots to accomplish long-horizon tasks in physical environments. In this work, we introduce EmbodiedGPT, an end-to-end multi-modal foundation model for embodied AI, empowering embodied agents with multi-modal understanding and execution capabilities. To achieve this, we have made the following efforts: (i) We craft a large-scale embodied planning dataset, termed EgoCOT. The dataset consists of carefully selected videos from the Ego4D dataset, along with corresponding high-quality language instructions. Specifically, we generate a sequence of sub-goals with the "Chain of Thoughts" mode for effective embodied planning. (ii) We introduce an efficient training approach to EmbodiedGPT for high-quality plan generation, by adapting a 7B large language model (LLM) to the EgoCOT dataset via prefix tuning. (iii) We introduce a paradigm for extracting task-related features from LLM-generated planning queries to form a closed loop between high-level planning and low-level control. Extensive experiments show the effectiveness of EmbodiedGPT on embodied tasks, including embodied planning, embodied control, visual captioning, and visual question answering. Notably, EmbodiedGPT significantly enhances the success rate of the embodied control task by extracting more effective features. It has achieved a remarkable 1.6 times increase in success rate on the Franka Kitchen benchmark and a 1.3 times increase on the Meta-World benchmark, compared to the BLIP-2 baseline fine-tuned with the Ego4D dataset.

Epilepsy is a prevalent neurological disorder that affects millions of individuals globally, and continuous monitoring coupled with automated seizure detection appears as a necessity for effective patient treatment. To enable long-term care in daily-life conditions, comfortable and smart wearable devices with long battery life are required, which in turn set the demand for resource-constrained and energy-efficient computing solutions. In this context, the development of machine learning algorithms for seizure detection faces the challenge of heavily imbalanced datasets. This paper introduces EpiDeNet, a new lightweight seizure detection network, and Sensitivity-Specificity Weighted Cross-Entropy (SSWCE), a new loss function that incorporates sensitivity and specificity, to address the challenge of heavily unbalanced datasets. The proposed EpiDeNet-SSWCE approach demonstrates the successful detection of 91.16% and 92.00% seizure events on two different datasets (CHB-MIT and PEDESITE, respectively), with only four EEG channels. A three-window majority voting-based smoothing scheme combined with the SSWCE loss achieves 3x reduction of false positives to 1.18 FP/h. EpiDeNet is well suited for implementation on low-power embedded platforms, and we evaluate its performance on two ARM Cortex-based platforms (M4F/M7) and two parallel ultra-low power (PULP) systems (GAP8, GAP9). The most efficient implementation (GAP9) achieves an energy efficiency of 40 GMAC/s/W, with an energy consumption per inference of only 0.051 mJ at high performance (726.46 MMAC/s), outperforming the best ARM Cortex-based solutions by approximately 160x in energy efficiency. The EpiDeNet-SSWCE method demonstrates effective and accurate seizure detection performance on heavily imbalanced datasets, while being suited for implementation on energy-constrained platforms.

The widespread integration of Internet of Things (IoT) devices across all facets of life has ushered in an era of interconnectedness, creating new avenues for cybersecurity challenges and underscoring the need for robust intrusion detection systems. However, traditional security systems are designed with a closed-world perspective and often face challenges in dealing with the ever-evolving threat landscape, where new and unfamiliar attacks are constantly emerging. In this paper, we introduce a framework aimed at mitigating the open set recognition (OSR) problem in the realm of Network Intrusion Detection Systems (NIDS) tailored for IoT environments. Our framework capitalizes on image-based representations of packet-level data, extracting spatial and temporal patterns from network traffic. Additionally, we integrate stacking and sub-clustering techniques, enabling the identification of unknown attacks by effectively modeling the complex and diverse nature of benign behavior. The empirical results prominently underscore the framework's efficacy, boasting an impressive 88\% detection rate for previously unseen attacks when compared against existing approaches and recent advancements. Future work will perform extensive experimentation across various openness levels and attack scenarios, further strengthening the adaptability and performance of our proposed solution in safeguarding IoT environments.

In many randomized experiments, the treatment effect of the long-term metric (i.e. the primary outcome of interest) is often difficult or infeasible to measure. Such long-term metrics are often slow to react to changes and sufficiently noisy they are challenging to faithfully estimate in short-horizon experiments. A common alternative is to measure several short-term proxy metrics in the hope they closely track the long-term metric -- so they can be used to effectively guide decision-making in the near-term. We introduce a new statistical framework to both define and construct an optimal proxy metric for use in a homogeneous population of randomized experiments. Our procedure first reduces the construction of an optimal proxy metric in a given experiment to a portfolio optimization problem which depends on the true latent treatment effects and noise level of experiment under consideration. We then denoise the observed treatment effects of the long-term metric and a set of proxies in a historical corpus of randomized experiments to extract estimates of the latent treatment effects for use in the optimization problem. One key insight derived from our approach is that the optimal proxy metric for a given experiment is not apriori fixed; rather it should depend on the sample size (or effective noise level) of the randomized experiment for which it is deployed. To instantiate and evaluate our framework, we employ our methodology in a large corpus of randomized experiments from an industrial recommendation system and construct proxy metrics that perform favorably relative to several baselines.

As one of the important tools for spatial feature extraction, graph convolution has been applied in a wide range of fields such as traffic flow prediction. However, current popular works of graph convolution cannot guarantee spatio-temporal consistency in a long period. The ignorance of correlational dynamics, convolutional locality and temporal comprehensiveness would limit predictive accuracy. In this paper, a novel Attention-based Dynamic Graph Convolutional Recurrent Neural Network (ADGCRNN) is proposed to improve traffic flow prediction in highway transportation. Three temporal resolutions of data sequence are effectively integrated by self-attention to extract characteristics; multi-dynamic graphs and their weights are dynamically created to compliantly combine the varying characteristics; a dedicated gated kernel emphasizing highly relative nodes is introduced on these complete graphs to reduce overfitting for graph convolution operations. Experiments on two public datasets show our work better than state-of-the-art baselines, and case studies of a real Web system prove practical benefit in highway transportation.

We designed and tested a system for real-time control of a user interface by extracting surface electromyographic (sEMG) activity from eight electrodes in a wrist-band configuration. sEMG data were streamed into a machine-learning algorithm that classified hand gestures in real-time. After an initial model calibration, participants were presented with one of three types of feedback during a human-learning stage: veridical feedback, in which predicted probabilities from the gesture classification algorithm were displayed without alteration, modified feedback, in which we applied a hidden augmentation of error to these probabilities, and no feedback. User performance was then evaluated in a series of minigames, in which subjects were required to use eight gestures to manipulate their game avatar to complete a task. Experimental results indicated that, relative to baseline, the modified feedback condition led to significantly improved accuracy and improved gesture class separation. These findings suggest that real-time feedback in a gamified user interface with manipulation of feedback may enable intuitive, rapid, and accurate task acquisition for sEMG-based gesture recognition applications.

In consequential domains, it is often impossible to compel individuals to take treatment, so that optimal policy rules are merely suggestions in the presence of human non-adherence to treatment recommendations. In these same domains, there may be heterogeneity both in who responds in taking-up treatment, and heterogeneity in treatment efficacy. While optimal treatment rules can maximize causal outcomes across the population, access parity constraints or other fairness considerations can be relevant in the case of encouragement. For example, in social services, a persistent puzzle is the gap in take-up of beneficial services among those who may benefit from them the most. When in addition the decision-maker has distributional preferences over both access and average outcomes, the optimal decision rule changes. We study causal identification, statistical variance-reduced estimation, and robust estimation of optimal treatment rules, including under potential violations of positivity. We consider fairness constraints such as demographic parity in treatment take-up, and other constraints, via constrained optimization. Our framework can be extended to handle algorithmic recommendations under an often-reasonable covariate-conditional exclusion restriction, using our robustness checks for lack of positivity in the recommendation. We develop a two-stage algorithm for solving over parametrized policy classes under general constraints to obtain variance-sensitive regret bounds. We illustrate the methods in two case studies based on data from randomized encouragement to enroll in insurance and from pretrial supervised release with electronic monitoring.

Clustering high-dimensional spatiotemporal data using an unsupervised approach is a challenging problem for many data-driven applications. Existing state-of-the-art methods for unsupervised clustering use different similarity and distance functions but focus on either spatial or temporal features of the data. Concentrating on joint deep representation learning of spatial and temporal features, we propose Deep Spatiotemporal Clustering (DSC), a novel algorithm for the temporal clustering of high-dimensional spatiotemporal data using an unsupervised deep learning method. Inspired by the U-net architecture, DSC utilizes an autoencoder integrating CNN-RNN layers to learn latent representations of the spatiotemporal data. DSC also includes a unique layer for cluster assignment on latent representations that uses the Student's t-distribution. By optimizing the clustering loss and data reconstruction loss simultaneously, the algorithm gradually improves clustering assignments and the nonlinear mapping between low-dimensional latent feature space and high-dimensional original data space. A multivariate spatiotemporal climate dataset is used to evaluate the efficacy of the proposed method. Our extensive experiments show our approach outperforms both conventional and deep learning-based unsupervised clustering algorithms. Additionally, we compared the proposed model with its various variants (CNN encoder, CNN autoencoder, CNN-RNN encoder, CNN-RNN autoencoder, etc.) to get insight into using both the CNN and RNN layers in the autoencoder, and our proposed technique outperforms these variants in terms of clustering results.

In deep learning, the load data with non-temporal factors are difficult to process by sequence models. This problem results in insufficient precision of the prediction. Therefore, a short-term load forecasting method based on convolutional neural network (CNN), self-attention encoder-decoder network (SAEDN) and residual-refinement (Res) is proposed. In this method, feature extraction module is composed of a two-dimensional convolutional neural network, which is used to mine the local correlation between data and obtain high-dimensional data features. The initial load fore-casting module consists of a self-attention encoder-decoder network and a feedforward neural network (FFN). The module utilizes self-attention mechanisms to encode high-dimensional features. This operation can obtain the global correlation between data. Therefore, the model is able to retain important information based on the coupling relationship between the data in data mixed with non-time series factors. Then, self-attention decoding is per-formed and the feedforward neural network is used to regression initial load. This paper introduces the residual mechanism to build the load optimization module. The module generates residual load values to optimize the initial load. The simulation results show that the proposed load forecasting method has advantages in terms of prediction accuracy and prediction stability.  ( 2 min )

Basecalling is an essential step in nanopore sequencing analysis where the raw signals of nanopore sequencers are converted into nucleotide sequences, i.e., reads. State-of-the-art basecallers employ complex deep learning models to achieve high basecalling accuracy. This makes basecalling computationally-inefficient and memory-hungry; bottlenecking the entire genome analysis pipeline. However, for many applications, the majority of reads do no match the reference genome of interest (i.e., target reference) and thus are discarded in later steps in the genomics pipeline, wasting the basecalling computation. To overcome this issue, we propose TargetCall, the first pre-basecalling filter to eliminate the wasted computation in basecalling. TargetCall's key idea is to discard reads that will not match the target reference (i.e., off-target reads) prior to basecalling. TargetCall consists of two main components: (1) LightCall, a lightweight neural network basecaller that produces noisy reads; and (2) Similarity Check, which labels each of these noisy reads as on-target or off-target by matching them to the target reference. TargetCall aims to filter out all off-target reads before basecalling. The highly-accurate but slow basecalling is performed only on the raw signals whose noisy reads are labeled as on-target. Our thorough experimental evaluations using both real and simulated data show that TargetCall 1) improves the end-to-end basecalling performance while maintaining high sensitivity in keeping on-target reads, 2) maintains high accuracy in downstream analysis, 3) precisely filters out up to 94.71% of off-target reads, and 4) achieves better performance, throughput, sensitivity, precision, and generality compared to prior works. We open-source TargetCall at https://github.com/CMU-SAFARI/TargetCall  ( 3 min )

Masked Image Modeling (MIM) methods, like Masked Autoencoders (MAE), efficiently learn a rich representation of the input. However, for adapting to downstream tasks, they require a sufficient amount of labeled data since their rich features code not only objects but also less relevant image background. In contrast, Instance Discrimination (ID) methods focus on objects. In this work, we study how to combine the efficiency and scalability of MIM with the ability of ID to perform downstream classification in the absence of large amounts of labeled data. To this end, we introduce Masked Autoencoder Contrastive Tuning (MAE-CT), a sequential approach that utilizes the implicit clustering of the Nearest Neighbor Contrastive Learning (NNCLR) objective to induce abstraction in the topmost layers of a pre-trained MAE. MAE-CT tunes the rich features such that they form semantic clusters of objects without using any labels. Notably, MAE-CT does not rely on hand-crafted augmentations and frequently achieves its best performances while using only minimal augmentations (crop & flip). Further, MAE-CT is compute efficient as it requires at most 10% overhead compared to MAE re-training. Applied to large and huge Vision Transformer (ViT) models, MAE-CT excels over previous self-supervised methods trained on ImageNet in linear probing, k-NN and low-shot classification accuracy as well as in unsupervised clustering accuracy. With ViT-H/16 MAE-CT achieves a new state-of-the-art in linear probing of 82.2%.  ( 3 min )

Variational algorithms require architectures that naturally constrain the optimisation space to run efficiently. In geometric quantum machine learning, one achieves this by encoding group structure into parameterised quantum circuits to include the symmetries of a problem as an inductive bias. However, constructing such circuits is challenging as a concrete guiding principle has yet to emerge. In this paper, we propose the use of spin networks, a form of directed tensor network invariant under a group transformation, to devise SU(2) equivariant quantum circuit ans\"atze -- circuits possessing spin rotation symmetry. By changing to the basis that block diagonalises SU(2) group action, these networks provide a natural building block for constructing parameterised equivariant quantum circuits. We prove that our construction is mathematically equivalent to other known constructions, such as those based on twirling and generalised permutations, but more direct to implement on quantum hardware. The efficacy of our constructed circuits is tested by solving the ground state problem of SU(2) symmetric Heisenberg models on the one-dimensional triangular lattice and on the Kagome lattice. Our results highlight that our equivariant circuits boost the performance of quantum variational algorithms, indicating broader applicability to other real-world problems.  ( 2 min )

Meteorological radar reflectivity data (i.e. radar echo) significantly influences precipitation prediction. It can facilitate accurate and expeditious forecasting of short-term heavy rainfall bypassing the need for complex Numerical Weather Prediction (NWP) models. In comparison to conventional models, Deep Learning (DL)-based radar echo extrapolation algorithms exhibit higher effectiveness and efficiency. Nevertheless, the development of reliable and generalized echo extrapolation algorithm is impeded by three primary challenges: cumulative error spreading, imprecise representation of sparsely distributed echoes, and inaccurate description of non-stationary motion processes. To tackle these challenges, this paper proposes a novel radar echo extrapolation algorithm called Temporal-Spatial Parallel Transformer, referred to as TempEE. TempEE avoids using auto-regression and instead employs a one-step forward strategy to prevent cumulative error spreading during the extrapolation process. Additionally, we propose the incorporation of a Multi-level Temporal-Spatial Attention mechanism to improve the algorithm's capability of capturing both global and local information while emphasizing task-related regions, including sparse echo representations, in an efficient manner. Furthermore, the algorithm extracts spatio-temporal representations from continuous echo images using a parallel encoder to model the non-stationary motion process for echo extrapolation. The superiority of our TempEE has been demonstrated in the context of the classic radar echo extrapolation task, utilizing a real-world dataset. Extensive experiments have further validated the efficacy and indispensability of various components within TempEE.  ( 3 min )

In the presence of right-censored data with covariates, the conditional Kaplan-Meier estimator (also known as the Beran estimator) consistently estimates the conditional survival function of the random follow-up for the event of interest. However, a necessary condition is the unambiguous knowledge of whether each individual is censored or not, which may be incomplete in practice. We therefore propose a study of the Beran estimator when the censoring indicators are generic random variables and discuss necessary conditions for the efficiency of the Beran estimator. From this, we provide a new estimator for the conditional survival function with missing not at random (MNAR) censoring indicators based on a conditional copula model for the missingness mechanism. In addition to the theoretical results, we illustrate how the estimators work for small samples through a simulation study and show their practical applicability by analyzing synthetic and real data.  ( 2 min )

Recently sequential recommendations and next-item prediction task has become increasingly popular in the field of recommender systems. Currently, two state-of-the-art baselines are Transformer-based models SASRec and BERT4Rec. Over the past few years, there have been quite a few publications comparing these two algorithms and proposing new state-of-the-art models. In most of the publications, BERT4Rec achieves better performance than SASRec. But BERT4Rec uses cross-entropy over softmax for all items, while SASRec uses negative sampling and calculates binary cross-entropy loss for one positive and one negative item. In our work, we show that if both models are trained with the same loss, which is used by BERT4Rec, then SASRec will significantly outperform BERT4Rec both in terms of quality and training speed. In addition, we show that SASRec could be effectively trained with negative sampling and still outperform BERT4Rec, but the number of negative examples should be much larger than one.  ( 2 min )

Automatic static cost analysis infers information about the resources used by programs without actually running them with concrete data, and presents such information as functions of input data sizes. Most of the analysis tools for logic programs (and other languages) are based on setting up recurrence relations representing (bounds on) the computational cost of predicates, and solving them to find closed-form functions that are equivalent to (or a bound on) them. Such recurrence solving is a bottleneck in current tools: many of the recurrences that arise during the analysis cannot be solved with current solvers, such as Computer Algebra Systems (CASs), so that specific methods for different classes of recurrences need to be developed. We address such a challenge by developing a novel, general approach for solving arbitrary, constrained recurrence relations, that uses machine-learning sparse regression techniques to guess a candidate closed-form function, and a combination of an SMT-solver and a CAS to check whether such function is actually a solution of the recurrence. We have implemented a prototype and evaluated it with recurrences generated by a cost analysis system (the one in CiaoPP). The experimental results are quite promising, showing that our approach can find closed-form solutions, in a reasonable time, for classes of recurrences that cannot be solved by such a system, nor by current CASs.  ( 3 min )

The task of preserving privacy while ensuring efficient communication is a fundamental challenge in federated learning. In this work, we tackle this challenge in the trusted aggregator model, and propose a solution that achieves both objectives simultaneously. We show that employing a quantization scheme based on subtractive dithering at the clients can effectively replicate the normal noise addition process at the aggregator. This implies that we can guarantee the same level of differential privacy against other clients while substantially reducing the amount of communication required, as opposed to transmitting full precision gradients and using central noise addition. We also experimentally demonstrate that the accuracy of our proposed approach matches that of the full precision gradient method.  ( 2 min )

Simplicial complexes prove effective in modeling data with multiway dependencies, such as data defined along the edges of networks or within other higher-order structures. Their spectrum can be decomposed into three interpretable subspaces via the Hodge decomposition, resulting foundational in numerous applications. We leverage this decomposition to develop a contrastive self-supervised learning approach for processing simplicial data and generating embeddings that encapsulate specific spectral information.Specifically, we encode the pertinent data invariances through simplicial neural networks and devise augmentations that yield positive contrastive examples with suitable spectral properties for downstream tasks. Additionally, we reweight the significance of negative examples in the contrastive loss, considering the similarity of their Hodge components to the anchor. By encouraging a stronger separation among less similar instances, we obtain an embedding space that reflects the spectral properties of the data. The numerical results on two standard edge flow classification tasks show a superior performance even when compared to supervised learning techniques. Our findings underscore the importance of adopting a spectral perspective for contrastive learning with higher-order data.  ( 2 min )

Quantum Support Vector Machines (QSVM) play a vital role in using quantum resources for supervised machine learning tasks, such as classification. However, current methods are strongly limited in terms of scalability on Noisy Intermediate Scale Quantum (NISQ) devices. In this work, we propose a novel approach called the Variational Quantum Linear Solver (VQLS) enhanced QSVM. This is built upon our idea of utilizing the variational quantum linear solver to solve system of linear equations of a least squares-SVM on a NISQ device. The implementation of our approach is evaluated by an extensive series of numerical experiments with the Iris dataset, which consists of three distinct iris plant species. Based on this, we explore the practicality and effectiveness of our algorithm by constructing a classifier capable of classification in a feature space ranging from one to seven dimensions. Furthermore, by strategically exploiting both classical and quantum computing for various subroutines of our algorithm, we effectively mitigate practical challenges associated with the implementation. These include significant improvement in the trainability of the variational ansatz and notable reductions in run-time for cost calculations. Based on the numerical experiments, our approach exhibits the capability of identifying a separating hyperplane in an 8-dimensional feature space. Moreover, it consistently demonstrated strong performance across various instances with the same dataset.  ( 2 min )

Historically, much of machine learning research has focused on the performance of the algorithm alone, but recently more attention has been focused on optimizing joint human-algorithm performance. Here, we analyze a specific type of human-algorithm collaboration where the algorithm has access to a set of $n$ items, and presents a subset of size $k$ to the human, who selects a final item from among those $k$. This scenario could model content recommendation, route planning, or any type of labeling task. Because both the human and algorithm have imperfect, noisy information about the true ordering of items, the key question is: which value of $k$ maximizes the probability that the best item will be ultimately selected? For $k=1$, performance is optimized by the algorithm acting alone, and for $k=n$ it is optimized by the human acting alone. Surprisingly, we show that for multiple of noise models, it is optimal to set $k \in [2, n-1]$ - that is, there are strict benefits to collaborating, even when the human and algorithm have equal accuracy separately. We demonstrate this theoretically for the Mallows model and experimentally for the Random Utilities models of noisy permutations. However, we show this pattern is reversed when the human is anchored on the algorithm's presented ordering - the joint system always has strictly worse performance. We extend these results to the case where the human and algorithm differ in their accuracy levels, showing that there always exist regimes where a more accurate agent would strictly benefit from collaborating with a less accurate one, but these regimes are asymmetric between the human and the algorithm's accuracy.  ( 3 min )

Ball bearings find widespread use in various manufacturing and mechanical domains, and methods based on machine learning have been widely adopted in the field to monitor wear and spot defects before they lead to failures. Few studies, however, have addressed the problem of censored data, in which failure is not observed. In this paper, we propose a novel approach to predict the time to failure in ball bearings using survival analysis. First, we analyze bearing data in the frequency domain and annotate when a bearing fails by comparing the Kullback-Leibler divergence and the standard deviation between its break-in frequency bins and its break-out frequency bins. Second, we train several survival models to estimate the time to failure based on the annotated data and covariates extracted from the time domain, such as skewness, kurtosis and entropy. The models give a probabilistic prediction of risk over time and allow us to compare the survival function between groups of bearings. We demonstrate our approach on the XJTU and PRONOSTIA datasets. On XJTU, the best result is a 0.70 concordance-index and 0.21 integrated Brier score. On PRONOSTIA, the best is a 0.76 concordance-index and 0.19 integrated Brier score. Our work motivates further work on incorporating censored data in models for predictive maintenance.  ( 2 min )

The recipe behind the success of deep learning has been the combination of neural networks and gradient-based optimization. Understanding the behavior of gradient descent however, and particularly its instability, has lagged behind its empirical success. To add to the theoretical tools available to study gradient descent we propose the principal flow (PF), a continuous time flow that approximates gradient descent dynamics. To our knowledge, the PF is the only continuous flow that captures the divergent and oscillatory behaviors of gradient descent, including escaping local minima and saddle points. Through its dependence on the eigendecomposition of the Hessian the PF sheds light on the recently observed edge of stability phenomena in deep learning. Using our new understanding of instability we propose a learning rate adaptation method which enables us to control the trade-off between training stability and test set evaluation performance.  ( 2 min )

Markov processes are widely used mathematical models for describing dynamic systems in various fields. However, accurately simulating large-scale systems at long time scales is computationally expensive due to the short time steps required for accurate integration. In this paper, we introduce an inference process that maps complex systems into a simplified representational space and models large jumps in time. To achieve this, we propose Time-lagged Information Bottleneck (T-IB), a principled objective rooted in information theory, which aims to capture relevant temporal features while discarding high-frequency information to simplify the simulation task and minimize the inference error. Our experiments demonstrate that T-IB learns information-optimal representations for accurately modeling the statistical properties and dynamics of the original process at a selected time lag, outperforming existing time-lagged dimensionality reduction methods.  ( 2 min )

We propose a physics-based method for synthesizing dexterous hand-object interactions in a full-body setting. While recent advancements have addressed specific facets of human-object interactions, a comprehensive physics-based approach remains a challenge. Existing methods often focus on isolated segments of the interaction process and rely on data-driven techniques that may result in artifacts. In contrast, our proposed method embraces reinforcement learning (RL) and physics simulation to mitigate the limitations of data-driven approaches. Through a hierarchical framework, we first learn skill priors for both body and hand movements in a decoupled setting. The generic skill priors learn to decode a latent skill embedding into the motion of the underlying part. A high-level policy then controls hand-object interactions in these pretrained latent spaces, guided by task objectives of grasping and 3D target trajectory following. It is trained using a novel reward function that combines an adversarial style term with a task reward, encouraging natural motions while fulfilling the task incentives. Our method successfully accomplishes the complete interaction task, from approaching an object to grasping and subsequent manipulation. We compare our approach against kinematics-based baselines and show that it leads to more physically plausible motions.  ( 2 min )

The increasing popularity of large language models (LLMs) has paved the way for their application in diverse domains. This paper proposes a benchmarking framework tailored specifically for evaluating LLM performance in the context of Verilog code generation for hardware design and verification. We present a comprehensive evaluation dataset consisting of 156 problems from the Verilog instructional website HDLBits. The evaluation set consists of a diverse set of Verilog code generation tasks, ranging from simple combinational circuits to complex finite state machines. The Verilog code completions can be automatically tested for functional correctness by comparing the transient simulation outputs of the generated design with a golden solution. We also demonstrate that the Verilog code generation capability of pretrained language models could be improved with supervised fine-tuning by bootstrapping with LLM generated synthetic problem-code pairs.  ( 2 min )

Wide use and availability of the machine learning and computer vision techniques allows development of relatively complex monitoring systems in many domains. Besides the traditional industrial domain, new application appears also in biology and agriculture, where we could speak about the detection of infections, parasites and weeds, but also about automated monitoring and early warning systems. This is also connected with the introduction of the easily accessible hardware and development kits such as Arduino, or RaspberryPi family. In this paper, we survey 50 existing papers focusing on the methods of automated beehive monitoring methods using the computer vision techniques, particularly on the pollen and Varroa mite detection together with the bee traffic monitoring. Such systems could also be used for the monitoring of the honeybee colonies and for the inspection of their health state, which could identify potentially dangerous states before the situation is critical, or to better plan periodic bee colony inspections and therefore save significant costs. Later, we also include analysis of the research trends in this application field and we outline the possible direction of the new explorations. Our paper is aimed also at veterinary and apidology professionals and experts, who might not be familiar with machine learning to introduce them to its possibilities, therefore each family of applications is opened by a brief theoretical introduction and motivation related to its base method. We hope that this paper will inspire other scientists to use machine learning techniques for other applications in beehive monitoring.  ( 3 min )

We study policy evaluation of offline contextual bandits subject to unobserved confounders. Sensitivity analysis methods are commonly used to estimate the policy value under the worst-case confounding over a given uncertainty set. However, existing work often resorts to some coarse relaxation of the uncertainty set for the sake of tractability, leading to overly conservative estimation of the policy value. In this paper, we propose a general estimator that provides a sharp lower bound of the policy value. It can be shown that our estimator contains the recently proposed sharp estimator by Dorn and Guo (2022) as a special case, and our method enables a novel extension of the classical marginal sensitivity model using f-divergence. To construct our estimator, we leverage the kernel method to obtain a tractable approximation to the conditional moment constraints, which traditional non-sharp estimators failed to take into account. In the theoretical analysis, we provide a condition for the choice of the kernel which guarantees no specification error that biases the lower bound estimation. Furthermore, we provide consistency guarantees of policy evaluation and learning. In the experiments with synthetic and real-world data, we demonstrate the effectiveness of the proposed method.  ( 2 min )

We introduce EarthPT -- an Earth Observation (EO) pretrained transformer. EarthPT is a 700 million parameter decoding transformer foundation model trained in an autoregressive self-supervised manner and developed specifically with EO use-cases in mind. We demonstrate that EarthPT is an effective forecaster that can accurately predict future pixel-level surface reflectances across the 400-2300 nm range well into the future. For example, forecasts of the evolution of the Normalised Difference Vegetation Index (NDVI) have a typical error of approximately 0.05 (over a natural range of -1 -> 1) at the pixel level over a five month test set horizon, out-performing simple phase-folded models based on historical averaging. We also demonstrate that embeddings learnt by EarthPT hold semantically meaningful information and could be exploited for downstream tasks such as highly granular, dynamic land use classification. Excitingly, we note that the abundance of EO data provides us with -- in theory -- quadrillions of training tokens. Therefore, if we assume that EarthPT follows neural scaling laws akin to those derived for Large Language Models (LLMs), there is currently no data-imposed limit to scaling EarthPT and other similar `Large Observation Models.'  ( 2 min )

The robotic manipulation of Deformable Linear Objects (DLOs) is a vital and challenging task that is important in many practical applications. Classical model-based approaches to this problem require an accurate model to capture how robot motions affect the deformation of the DLO. Nowadays, data-driven models offer the best tradeoff between quality and computation time. This paper analyzes several learning-based 3D models of the DLO and proposes a new one based on the Transformer architecture that achieves superior accuracy, even on the DLOs of different lengths, thanks to the proposed scaling method. Moreover, we introduce a data augmentation technique, which improves the prediction performance of almost all considered DLO data-driven models. Thanks to this technique, even a simple Multilayer Perceptron (MLP) achieves close to state-of-the-art performance while being significantly faster to evaluate. In the experiments, we compare the performance of the learning-based 3D models of the DLO on several challenging datasets quantitatively and demonstrate their applicability in the task of shaping a DLO.  ( 2 min )

While real-world applications of reinforcement learning are becoming popular, the security and robustness of RL systems are worthy of more attention and exploration. In particular, recent works have revealed that, in a multi-agent RL environment, backdoor trigger actions can be injected into a victim agent (a.k.a. Trojan agent), which can result in a catastrophic failure as soon as it sees the backdoor trigger action. To ensure the security of RL agents against malicious backdoors, in this work, we propose the problem of Backdoor Detection in a multi-agent competitive reinforcement learning system, with the objective of detecting Trojan agents as well as the corresponding potential trigger actions, and further trying to mitigate their Trojan behavior. In order to solve this problem, we propose PolicyCleanse that is based on the property that the activated Trojan agents accumulated rewards degrade noticeably after several timesteps. Along with PolicyCleanse, we also design a machine unlearning-based approach that can effectively mitigate the detected backdoor. Extensive experiments demonstrate that the proposed methods can accurately detect Trojan agents, and outperform existing backdoor mitigation baseline approaches by at least 3% in winning rate across various types of agents and environments.  ( 3 min )

Federated learning algorithms perform reasonably well on independent and identically distributed (IID) data. They, on the other hand, suffer greatly from heterogeneous environments, i.e., Non-IID data. Despite the fact that many research projects have been done to address this issue, recent findings indicate that they are still sub-optimal when compared to training on IID data. In this work, we carefully analyze the existing methods in heterogeneous environments. Interestingly, we find that regularizing the classifier's outputs is quite effective in preventing performance degradation on Non-IID data. Motivated by this, we propose Learning from Drift (LfD), a novel method for effectively training the model in heterogeneous settings. Our scheme encapsulates two key components: drift estimation and drift regularization. Specifically, LfD first estimates how different the local model is from the global model (i.e., drift). The local model is then regularized such that it does not fall in the direction of the estimated drift. In the experiment, we evaluate each method through the lens of the five aspects of federated learning, i.e., Generalization, Heterogeneity, Scalability, Forgetting, and Efficiency. Comprehensive evaluation results clearly support the superiority of LfD in federated learning with Non-IID data.  ( 2 min )

We present a Split Vector Quantized Variational Autoencoder (SVQ-VAE) architecture using a split vector quantizer for NTTS, as an enhancement to the well-known Variational Autoencoder (VAE) and Vector Quantized Variational Autoencoder (VQ-VAE) architectures. Compared to these previous architectures, our proposed model retains the benefits of using an utterance-level bottleneck, while keeping significant representation power and a discretized latent space small enough for efficient prediction from text. We train the model on recordings in the expressive task-oriented dialogues domain and show that SVQ-VAE achieves a statistically significant improvement in naturalness over the VAE and VQ-VAE models. Furthermore, we demonstrate that the SVQ-VAE latent acoustic space is predictable from text, reducing the gap between the standard constant vector synthesis and vocoded recordings by 32%.  ( 2 min )

Conducting cognitive tests is time-consuming for patients and clinicians. Wearable device-based prediction models allow for continuous health monitoring under normal living conditions and could offer an alternative to identifying older adults with cognitive impairments for early interventions. In this study, we first derived novel wearable-based features related to circadian rhythms, ambient light exposure, physical activity levels, sleep, and signal processing. Then, we quantified the ability of wearable-based machine-learning models to predict poor cognition based on outcomes from the Digit Symbol Substitution Test (DSST), the Consortium to Establish a Registry for Alzheimers Disease Word-Learning subtest (CERAD-WL), and the Animal Fluency Test (AFT). We found that the wearable-based models had significantly higher AUCs when predicting all three cognitive outcomes compared to benchmark models containing age, sex, education, marital status, household income, diabetic status, depression symptoms, and functional independence scores. In addition to uncovering previously unidentified wearable-based features that are predictive of poor cognition such as the standard deviation of the midpoints of each persons most active 10-hour periods and least active 5-hour periods, our paper provides proof-of-concept that wearable-based machine learning models can be used to autonomously screen older adults for possible cognitive impairments. Such models offer cost-effective alternatives to conducting initial screenings manually in clinical settings.  ( 2 min )

Integrating variable renewable energy into the grid has posed challenges to system operators in achieving optimal trade-offs among energy availability, cost affordability, and pollution controllability. This paper proposes a multi-agent reinforcement learning framework for managing energy transactions in microgrids. The framework addresses the challenges above: it seeks to optimize the usage of available resources by minimizing the carbon footprint while benefiting all stakeholders. The proposed architecture consists of three layers of agents, each pursuing different objectives. The first layer, comprised of prosumers and consumers, minimizes the total energy cost. The other two layers control the energy price to decrease the carbon impact while balancing the consumption and production of both renewable and conventional energy. This framework also takes into account fluctuations in energy demand and supply.  ( 2 min )

A growing body of research has focused on the Reinforcement Learning (RL) methods which allow the agent to learn from trial-and-error experiences gathered during the interaction with the environment. Recently, offline RL becomes a popular RL paradigm because it saves the interactions with environments. In offline RL, data providers share large pre-collected datasets, and others can train high-quality agents without interacting with the environments. This paradigm has demonstrated effectiveness in critical tasks like robot control, autonomous driving, etc. However, less attention is paid to investigating the security threats to the offline RL system. This paper focuses on backdoor attacks, where some perturbations are added to the data (observations) such that given normal observations, the agent takes high-rewards actions, and low-reward actions on observations injected with triggers. In this paper, we propose Baffle (Backdoor Attack for Offline Reinforcement Learning), an approach that automatically implants backdoors to RL agents by poisoning the offline RL dataset, and evaluate how different offline RL algorithms react to this attack. Our experiments conducted on four tasks and four offline RL algorithms expose a disquieting fact: none of the existing offline RL algorithms is immune to such a backdoor attack. Baffle modifies $10\%$ of the datasets for four tasks. Agents trained on the poisoned datasets perform well in normal settings. However, when triggers are presented, the agents' performance decreases drastically by $63.2\%$, $53.9\%$, $64.7\%$, and $47.4\%$ in the four tasks on average. The backdoor still persists after fine-tuning poisoned agents on clean datasets. We further show that the inserted backdoor is also hard to be detected by a popular defensive method. This paper calls attention to developing more effective protection for the open-source offline RL dataset.  ( 3 min )

Not being able to understand and predict the behavior of deep learning systems makes it hard to decide what architecture and algorithm to use for a given problem. In science and engineering, modeling is a methodology used to understand complex systems whose internal processes are opaque. Modeling replaces a complex system with a simpler, more interpretable surrogate. Drawing inspiration from this, we construct a class of surrogate models for neural networks using Gaussian processes. Rather than deriving kernels for infinite neural networks, we learn kernels empirically from the naturalistic behavior of finite neural networks. We demonstrate our approach captures existing phenomena related to the spectral bias of neural networks, and then show that our surrogate models can be used to solve practical problems such as identifying which points most influence the behavior of specific neural networks and predicting which architectures and algorithms will generalize well for specific datasets.  ( 2 min )

In the present paper we introduce new optimization algorithms for the task of density ratio estimation. More precisely, we consider extending the well-known KMM method using the construction of a suitable loss function, in order to encompass more general situations involving the estimation of density ratio with respect to subsets of the training data and test data, respectively. The associated codes can be found at https://github.com/CDAlecsa/Generalized-KMM.  ( 2 min )

In machine learning models, the estimation of errors is often complex due to distribution bias, particularly in spatial data such as those found in environmental studies. We introduce an approach based on the ideas of importance sampling to obtain an unbiased estimate of the target error. By taking into account difference between desirable error and available data, our method reweights errors at each sample point and neutralizes the shift. Importance sampling technique and kernel density estimation were used for reweighteing. We validate the effectiveness of our approach using artificial data that resemble real-world spatial datasets. Our findings demonstrate advantages of the proposed approach for the estimation of the target error, offering a solution to a distribution shift problem. Overall error of predictions dropped from 7% to just 2% and it gets smaller for larger samples.  ( 2 min )

In recent years, precision agriculture has gradually oriented farming closer to automation processes to support all the activities related to field management. Service robotics plays a predominant role in this evolution by deploying autonomous agents that can navigate fields while performing tasks without human intervention, such as monitoring, spraying, and harvesting. To execute these precise actions, mobile robots need a real-time perception system that understands their surroundings and identifies their targets in the wild. Generalizing to new crops and environmental conditions is critical for practical applications, as labeled samples are rarely available. In this paper, we investigate the problem of crop segmentation and propose a novel approach to enhance domain generalization using knowledge distillation. In the proposed framework, we transfer knowledge from an ensemble of models individually trained on source domains to a student model that can adapt to unseen target domains. To evaluate the proposed method, we present a synthetic multi-domain dataset for crop segmentation containing plants of variegate shapes and covering different terrain styles, weather conditions, and light scenarios for more than 50,000 samples. We demonstrate significant improvements in performance over state-of-the-art methods and superior sim-to-real generalization. Our approach provides a promising solution for domain generalization in crop segmentation and has the potential to enhance a wide variety of precision agriculture applications.  ( 2 min )

We study the classical problem of approximating a non-decreasing function $f: \mathcal{X} \to \mathcal{Y}$ in $L^p(\mu)$ norm by sequentially querying its values, for known compact real intervals $\mathcal{X}$, $\mathcal{Y}$ and a known probability measure $\mu$ on $\cX$. For any function~$f$ we characterize the minimum number of evaluations of $f$ that algorithms need to guarantee an approximation $\hat{f}$ with an $L^p(\mu)$ error below $\epsilon$ after stopping. Unlike worst-case results that hold uniformly over all $f$, our complexity measure is dependent on each specific function $f$. To address this problem, we introduce GreedyBox, a generalization of an algorithm originally proposed by Novak (1992) for numerical integration. We prove that GreedyBox achieves an optimal sample complexity for any function $f$, up to logarithmic factors. Additionally, we uncover results regarding piecewise-smooth functions. Perhaps as expected, the $L^p(\mu)$ error of GreedyBox decreases much faster for piecewise-$C^2$ functions than predicted by the algorithm (without any knowledge on the smoothness of $f$). A simple modification even achieves optimal minimax approximation rates for such functions, which we compute explicitly. In particular, our findings highlight multiple performance gaps between adaptive and non-adaptive algorithms, smooth and piecewise-smooth functions, as well as monotone or non-monotone functions. Finally, we provide numerical experiments to support our theoretical results.  ( 2 min )

In real life, success is often contingent upon multiple critical steps that are distant in time from each other and from the final reward. These critical steps are challenging to identify with traditional reinforcement learning (RL) methods that rely on the Bellman equation for credit assignment. Here, we present a new RL algorithm that uses offline contrastive learning to hone in on critical steps. This algorithm, which we call contrastive introspection (ConSpec), can be added to any existing RL algorithm. ConSpec learns a set of prototypes for the critical steps in a task by a novel contrastive loss and delivers an intrinsic reward when the current state matches one of these prototypes. The prototypes in ConSpec provide two key benefits for credit assignment: (1) They enable rapid identification of all the critical steps. (2) They do so in a readily interpretable manner, enabling out-of-distribution generalization when sensory features are altered. Distinct from other contemporary RL approaches to credit assignment, ConSpec takes advantage of the fact that it is easier to retrospectively identify the small set of steps that success is contingent upon than it is to prospectively predict reward at every step taken in the environment. Altogether, ConSpec improves learning in a diverse set of RL tasks, including both those with explicit, discrete critical steps and those with complex, continuous critical steps.  ( 3 min )

Large language models (LLMs) have played a pivotal role in revolutionizing various facets of our daily existence. Solving attention regression is a fundamental task in optimizing LLMs. In this work, we focus on giving a provable guarantee for the one-layer attention network objective function $L(X,Y) = \sum_{j_0 = 1}^n \sum_{i_0 = 1}^d ( \langle \langle \exp( \mathsf{A}_{j_0} x ) , {\bf 1}_n \rangle^{-1} \exp( \mathsf{A}_{j_0} x ), A_{3} Y_{*,i_0} \rangle - b_{j_0,i_0} )^2$. Here $\mathsf{A} \in \mathbb{R}^{n^2 \times d^2}$ is Kronecker product between $A_1 \in \mathbb{R}^{n \times d}$ and $A_2 \in \mathbb{R}^{n \times d}$. $A_3$ is a matrix in $\mathbb{R}^{n \times d}$, $\mathsf{A}_{j_0} \in \mathbb{R}^{n \times d^2}$ is the $j_0$-th block of $\mathsf{A}$. The $X, Y \in \mathbb{R}^{d \times d}$ are variables we want to learn. $B \in \mathbb{R}^{n \times d}$ and $b_{j_0,i_0} \in \mathbb{R}$ is one entry at $j_0$-th row and $i_0$-th column of $B$, $Y_{*,i_0} \in \mathbb{R}^d$ is the $i_0$-column vector of $Y$, and $x \in \mathbb{R}^{d^2}$ is the vectorization of $X$. In a multi-layer LLM network, the matrix $B \in \mathbb{R}^{n \times d}$ can be viewed as the output of a layer, and $A_1= A_2 = A_3 \in \mathbb{R}^{n \times d}$ can be viewed as the input of a layer. The matrix version of $x$ can be viewed as $QK^\top$ and $Y$ can be viewed as $V$. We provide an iterative greedy algorithm to train loss function $L(X,Y)$ up $\epsilon$ that runs in $\widetilde{O}( ({\cal T}_{\mathrm{mat}}(n,n,d) + {\cal T}_{\mathrm{mat}}(n,d,d) + d^{2\omega}) \log(1/\epsilon) )$ time. Here ${\cal T}_{\mathrm{mat}}(a,b,c)$ denotes the time of multiplying $a \times b$ matrix another $b \times c$ matrix, and $\omega\approx 2.37$ denotes the exponent of matrix multiplication.  ( 3 min )

Hurricanes present major challenges in the U.S. due to their devastating impacts. Mitigating these risks is important, and the insurance industry is central in this effort, using intricate statistical models for risk assessment. However, these models often neglect key temporal and spatial hurricane patterns and are limited by data scarcity. This study introduces a refined approach combining the ARIMA model and K-MEANS to better capture hurricane trends, and an Autoencoder for enhanced hurricane simulations. Our experiments show that this hybrid methodology effectively simulate historical hurricane behaviors while providing detailed projections of potential future trajectories and intensities. Moreover, by leveraging a comprehensive yet selective dataset, our simulations enrich the current understanding of hurricane patterns and offer actionable insights for risk management strategies.  ( 2 min )

Deep neural networks have shown many fruitful applications in this decade. A network can get the generalized function through training with a finite dataset. The degree of generalization is a realization of the proximity scale in the data space. Specifically, the scale is not clear if the dataset is complicated. Here we consider a network for the distribution estimation of the dataset. We show the estimation is unstable and the instability depends on the data density and training duration. We derive the kernel-balanced equation, which gives a short phenomenological description of the solution. The equation tells us the reason for the instability and the mechanism of the scale. The network outputs a local average of the dataset as a prediction and the scale of averaging is determined along the equation. The scale gradually decreases along training and finally results in instability in our case.  ( 2 min )

Knowledge Graphs (KGs) often have two characteristics: heterogeneous graph structure and text-rich entity/relation information. Text-based KG embeddings can represent entities by encoding descriptions with pre-trained language models, but no open-sourced library is specifically designed for KGs with PLMs at present. In this paper, we present LambdaKG, a library for KGE that equips with many pre-trained language models (e.g., BERT, BART, T5, GPT-3), and supports various tasks (e.g., knowledge graph completion, question answering, recommendation, and knowledge probing). LambdaKG is publicly open-sourced at https://github.com/zjunlp/PromptKG/tree/main/lambdaKG, with a demo video at this http URL and long-term maintenance.  ( 2 min )

We consider the problem of learning an inner approximation of the region of attraction (ROA) of an asymptotically stable equilibrium point without an explicit model of the dynamics. Rather than leveraging approximate models with bounded uncertainty to find a (robust) invariant set contained in the ROA, we propose to learn sets that satisfy a more relaxed notion of containment known as recurrence. We define a set to be $\tau$-recurrent (resp. $k$-recurrent) if every trajectory that starts within the set, returns to it after at most $\tau$ seconds (resp. $k$ steps). We show that under mild assumptions a $\tau$-recurrent set containing a stable equilibrium must be a subset of its ROA. We then leverage this property to develop algorithms that compute inner approximations of the ROA using counter-examples of recurrence that are obtained by sampling finite-length trajectories. Our algorithms process samples sequentially, which allow them to continue being executed even after an initial offline training stage. We further provide an upper bound on the number of counter-examples used by the algorithm, and almost sure convergence guarantees.  ( 2 min )

Recent advances in learning reusable motion priors have demonstrated their effectiveness in generating naturalistic behaviors. In this paper, we propose a new learning framework in this paradigm for controlling physics-based characters with significantly improved motion quality and diversity over existing state-of-the-art methods. The proposed method uses reinforcement learning (RL) to initially track and imitate life-like movements from unstructured motion clips using the discrete information bottleneck, as adopted in the Vector Quantized Variational AutoEncoder (VQ-VAE). This structure compresses the most relevant information from the motion clips into a compact yet informative latent space, i.e., a discrete space over vector quantized codes. By sampling codes in the space from a trained categorical prior distribution, high-quality life-like behaviors can be generated, similar to the usage of VQ-VAE in computer vision. Although this prior distribution can be trained with the supervision of the encoder's output, it follows the original motion clip distribution in the dataset and could lead to imbalanced behaviors in our setting. To address the issue, we further propose a technique named prior shifting to adjust the prior distribution using curiosity-driven RL. The outcome distribution is demonstrated to offer sufficient behavioral diversity and significantly facilitates upper-level policy learning for downstream tasks. We conduct comprehensive experiments using humanoid characters on two challenging downstream tasks, sword-shield striking and two-player boxing game. Our results demonstrate that the proposed framework is capable of controlling the character to perform considerably high-quality movements in terms of behavioral strategies, diversity, and realism. Videos, codes, and data are available at https://tencent-roboticsx.github.io/NCP/.  ( 3 min )

Community question answering (CQA) forums are Internet-based platforms where users ask questions about a topic and other expert users try to provide solutions. Many CQA forums such as Quora, Stackoverflow, Yahoo!Answer, StackExchange exist with a lot of user-generated data. These data are leveraged in automated CQA ranking systems where similar questions (and answers) are presented in response to the query of the user. In this work, we empirically investigate a few aspects of this domain. Firstly, in addition to traditional features like TF-IDF, BM25 etc., we introduce a BERT-based feature that captures the semantic similarity between the question and answer. Secondly, most of the existing research works have focused on features extracted only from the question part; features extracted from answers have not been explored extensively. We combine both types of features in a linear fashion. Thirdly, using our proposed concepts, we conduct an empirical investigation with different rank-learning algorithms, some of which have not been used so far in CQA domain. On three standard CQA datasets, our proposed framework achieves state-of-the-art performance. We also analyze importance of the features we use in our investigation. This work is expected to guide the practitioners to select a better set of features for the CQA retrieval task.  ( 2 min )

Smart Ice Cloud Sensing (SMICES) is a small-sat concept in which a primary radar intelligently targets ice storms based on information collected by a lookahead radiometer. Critical to the intelligent targeting is accurate identification of storm/cloud types from eight bands of radiance collected by the radiometer. The cloud types of interest are: clear sky, thin cirrus, cirrus, rainy anvil, and convection core. We describe multi-step use of Machine Learning and Digital Twin of the Earth's atmosphere to derive such a classifier. First, a digital twin of Earth's atmosphere called a Weather Research Forecast (WRF) is used generate simulated lookahead radiometer data as well as deeper "science" hidden variables. The datasets simulate a tropical region over the Caribbean and a non-tropical region over the Atlantic coast of the United States. A K-means clustering over the scientific hidden variables was utilized by human experts to generate an automatic labelling of the data - mapping each physical data point to cloud types by scientists informed by mean/centroids of hidden variables of the clusters. Next, classifiers were trained with the inputs of the simulated radiometer data and its corresponding label. The classifiers of a random decision forest (RDF), support vector machine (SVM), Gaussian na\"ive bayes, feed forward artificial neural network (ANN), and a convolutional neural network (CNN) were trained. Over the tropical dataset, the best performing classifier was able to identify non-storm and storm clouds with over 80% accuracy in each class for a held-out test set. Over the non-tropical dataset, the best performing classifier was able to classify non-storm clouds with over 90% accuracy and storm clouds with over 40% accuracy. Additionally both sets of classifiers were shown to be resilient to instrument noise.  ( 3 min )

Open-ended learning benefits immensely from the use of symbolic methods for goal representation as they offer ways to structure knowledge for efficient and transferable learning. However, the existing Hierarchical Reinforcement Learning (HRL) approaches relying on symbolic reasoning are often limited as they require a manual goal representation. The challenge in autonomously discovering a symbolic goal representation is that it must preserve critical information, such as the environment dynamics. In this work, we propose a developmental mechanism for subgoal discovery via an emergent representation that abstracts (i.e., groups together) sets of environment states that have similar roles in the task. We create a HRL algorithm that gradually learns this representation along with the policies and evaluate it on navigation tasks to show the learned representation is interpretable and results in data efficiency.  ( 2 min )

Nuclear Magnetic Resonance (NMR) spectroscopy has served as a powerful analytical tool for studying molecular structure and dynamics in chemistry and biology. However, the processing of raw data acquired from NMR spectrometers and subsequent quantitative analysis involves various specialized tools, which necessitates comprehensive knowledge in programming and NMR. Particularly, the emerging deep learning tools is hard to be widely used in NMR due to the sophisticated setup of computation. Thus, NMR processing is not an easy task for chemist and biologists. In this work, we present CloudBrain-NMR, an intelligent online cloud computing platform designed for NMR data reading, processing, reconstruction, and quantitative analysis. The platform is conveniently accessed through a web browser, eliminating the need for any program installation on the user side. CloudBrain-NMR uses parallel computing with graphics processing units and central processing units, resulting in significantly shortened computation time. Furthermore, it incorporates state-of-the-art deep learning-based algorithms offering comprehensive functionalities that allow users to complete the entire processing procedure without relying on additional software. This platform has empowered NMR applications with advanced artificial intelligence processing. CloudBrain-NMR is openly accessible for free usage at https://csrc.xmu.edu.cn/CloudBrain.html  ( 2 min )

Financial simulators play an important role in enhancing forecasting accuracy, managing risks, and fostering strategic financial decision-making. Despite the development of financial market simulation methodologies, existing frameworks often struggle with adapting to specialized simulation context. We pinpoint the challenges as i) current financial datasets do not contain context labels; ii) current techniques are not designed to generate financial data with context as control, which demands greater precision compared to other modalities; iii) the inherent difficulties in generating context-aligned, high-fidelity data given the non-stationary, noisy nature of financial data. To address these challenges, our contributions are: i) we proposed the Contextual Market Dataset with market dynamics, stock ticker, and history state as context, leveraging a market dynamics modeling method that combines linear regression and Dynamic Time Warping clustering to extract market dynamics; ii) we present Market-GAN, a novel architecture incorporating a Generative Adversarial Networks (GAN) for the controllable generation with context, an autoencoder for learning low-dimension features, and supervisors for knowledge transfer; iii) we introduce a two-stage training scheme to ensure that Market-GAN captures the intrinsic market distribution with multiple objectives. In the pertaining stage, with the use of the autoencoder and supervisors, we prepare the generator with a better initialization for the adversarial training stage. We propose a set of holistic evaluation metrics that consider alignment, fidelity, data usability on downstream tasks, and market facts. We evaluate Market-GAN with the Dow Jones Industrial Average data from 2000 to 2023 and showcase superior performance in comparison to 4 state-of-the-art time-series generative models.  ( 3 min )

Transformers have significantly advanced the field of natural language processing, but comprehending their internal mechanisms remains a challenge. In this paper, we introduce a novel geometric perspective that elucidates the inner mechanisms of transformer operations. Our primary contribution is illustrating how layer normalization confines the latent features to a hyper-sphere, subsequently enabling attention to mold the semantic representation of words on this surface. This geometric viewpoint seamlessly connects established properties such as iterative refinement and contextual embeddings. We validate our insights by probing a pre-trained 124M parameter GPT-2 model. Our findings reveal clear query-key attention patterns in early layers and build upon prior observations regarding the subject-specific nature of attention heads at deeper layers. Harnessing these geometric insights, we present an intuitive understanding of transformers, depicting them as processes that model the trajectory of word particles along the hyper-sphere.  ( 2 min )

In this work, we provide a characterization of the feature-learning process in two-layer ReLU networks trained by gradient descent on the logistic loss following random initialization. We consider data with binary labels that are generated by an XOR-like function of the input features. We permit a constant fraction of the training labels to be corrupted by an adversary. We show that, although linear classifiers are no better than random guessing for the distribution we consider, two-layer ReLU networks trained by gradient descent achieve generalization error close to the label noise rate. We develop a novel proof technique that shows that at initialization, the vast majority of neurons function as random features that are only weakly correlated with useful features, and the gradient descent dynamics 'amplify' these weak, random features to strong, useful features.

Score-based methods for learning Bayesain networks(BN) aim to maximizing the global score functions. However, if local variables have direct and indirect dependence simultaneously, the global optimization on score functions misses edges between variables with indirect dependent relationship, of which scores are smaller than those with direct dependent relationship. In this paper, we present an identifiability condition based on a determined subset of parents to identify the underlying DAG. By the identifiability condition, we develop a two-phase algorithm namely optimal-tuning (OT) algorithm to locally amend the global optimization. In the optimal phase, an optimization problem based on first-order Hilbert-Schmidt independence criterion (HSIC) gives an estimated skeleton as the initial determined parents subset. In the tuning phase, the skeleton is locally tuned by deletion, addition and DAG-formalization strategies using the theoretically proved incremental properties of high-order HSIC. Numerical experiments for different synthetic datasets and real-world datasets show that the OT algorithm outperforms existing methods. Especially in Sigmoid Mix model with the size of the graph being ${\rm\bf d=40}$, the structure intervention distance (SID) of the OT algorithm is 329.7 smaller than the one obtained by CAM, which indicates that the graph estimated by the OT algorithm misses fewer edges compared with CAM.Source code of the OT algorithm is available at https://github.com/YafeiannWang/optimal-tune-algorithm.

The myriad complex systems with multiway interactions motivate the extension of graph-based pairwise connections to higher-order relations. In particular, the simplicial complex has inspired generalizations of graph neural networks (GNNs) to simplicial complex-based models. Learning on such systems requires large amounts of data, which can be expensive or impossible to obtain. We propose data augmentation of simplicial complexes through both linear and nonlinear mixup mechanisms that return mixtures of existing labeled samples. In addition to traditional pairwise mixup, we present a convex clustering mixup approach for a data-driven relationship among several simplicial complexes. We theoretically demonstrate that the resultant synthetic simplicial complexes interpolate among existing data with respect to homomorphism densities. Our method is demonstrated on both synthetic and real-world datasets for simplicial complex classification.

In this paper, we consider the intersection of two problems in machine learning: Multi-Source Domain Adaptation (MSDA) and Dataset Distillation (DD). On the one hand, the first considers adapting multiple heterogeneous labeled source domains to an unlabeled target domain. On the other hand, the second attacks the problem of synthesizing a small summary containing all the information about the datasets. We thus consider a new problem called MSDA-DD. To solve it, we adapt previous works in the MSDA literature, such as Wasserstein Barycenter Transport and Dataset Dictionary Learning, as well as DD method Distribution Matching. We thoroughly experiment with this novel problem on four benchmarks (Caltech-Office 10, Tennessee-Eastman Process, Continuous Stirred Tank Reactor, and Case Western Reserve University), where we show that, even with as little as 1 sample per class, one achieves state-of-the-art adaptation performance.

Not being able to understand and predict the behavior of deep learning systems makes it hard to decide what architecture and algorithm to use for a given problem. In science and engineering, modeling is a methodology used to understand complex systems whose internal processes are opaque. Modeling replaces a complex system with a simpler, more interpretable surrogate. Drawing inspiration from this, we construct a class of surrogate models for neural networks using Gaussian processes. Rather than deriving kernels for infinite neural networks, we learn kernels empirically from the naturalistic behavior of finite neural networks. We demonstrate our approach captures existing phenomena related to the spectral bias of neural networks, and then show that our surrogate models can be used to solve practical problems such as identifying which points most influence the behavior of specific neural networks and predicting which architectures and algorithms will generalize well for specific datasets.

In the Variational Autoencoder (VAE), the variational posterior often aligns closely with the prior, which is known as posterior collapse and hinders the quality of representation learning. To mitigate this problem, an adjustable hyperparameter beta has been introduced in the VAE. This paper presents a closed-form expression to assess the relationship between the beta in VAE, the dataset size, the posterior collapse, and the rate-distortion curve by analyzing a minimal VAE in a high-dimensional limit. These results clarify that a long plateau in the generalization error emerges with a relatively larger beta. As the beta increases, the length of the plateau extends and then becomes infinite beyond a certain beta threshold. This implies that the choice of beta, unlike the usual regularization parameters, can induce posterior collapse regardless of the dataset size. Thus, beta is a risky parameter that requires careful tuning. Furthermore, considering the dataset-size dependence on the rate-distortion curve, a relatively large dataset is required to obtain a rate-distortion curve with high rates. Extensive numerical experiments support our analysis.

Epidemiological models must be calibrated to ground truth for downstream tasks such as producing forward projections or running what-if scenarios. The meaning of calibration changes in case of a stochastic model since output from such a model is generally described via an ensemble or a distribution. Each member of the ensemble is usually mapped to a random number seed (explicitly or implicitly). With the goal of finding not only the input parameter settings but also the random seeds that are consistent with the ground truth, we propose a class of Gaussian process (GP) surrogates along with an optimization strategy based on Thompson sampling. This Trajectory Oriented Optimization (TOO) approach produces actual trajectories close to the empirical observations instead of a set of parameter settings where only the mean simulation behavior matches with the ground truth.

We define a family of $C^1$ functions which we call "nowhere coexpanding functions" that is closed under composition and includes all $C^3$ functions with non-positive Schwarzian derivative. We establish results on the number and nature of the fixed points of these functions, including a generalisation of a classic result of Singer.

Tens of thousands of simultaneous hypothesis tests are routinely performed in genomic studies to identify differentially expressed genes. However, due to unmeasured confounders, many standard statistical approaches may be substantially biased. This paper investigates the large-scale hypothesis testing problem for multivariate generalized linear models in the presence of confounding effects. Under arbitrary confounding mechanisms, we propose a unified statistical estimation and inference framework that harnesses orthogonal structures and integrates linear projections into three key stages. It first leverages multivariate responses to separate marginal and uncorrelated confounding effects, recovering the confounding coefficients' column space. Subsequently, latent factors and primary effects are jointly estimated, utilizing $\ell_1$-regularization for sparsity while imposing orthogonality onto confounding coefficients. Finally, we incorporate projected and weighted bias-correction steps for hypothesis testing. Theoretically, we establish various effects' identification conditions and non-asymptotic error bounds. We show effective Type-I error control of asymptotic $z$-tests as sample and response sizes approach infinity. Numerical experiments demonstrate that the proposed method controls the false discovery rate by the Benjamini-Hochberg procedure and is more powerful than alternative methods. By comparing single-cell RNA-seq counts from two groups of samples, we demonstrate the suitability of adjusting confounding effects when significant covariates are absent from the model.

We develop a theoretical framework for $S_n$-equivariant convolutional quantum circuits with SU$(d)$-symmetry, building on and significantly generalizing Jordan's Permutational Quantum Computing (PQC) formalism based on Schur-Weyl duality connecting both SU$(d)$ and $S_n$ actions on qudits. In particular, we utilize the Okounkov-Vershik approach to prove Harrow's statement (Ph.D. Thesis 2005 p.160) on the equivalence between $\operatorname{SU}(d)$ and $S_n$ irrep bases and to establish the $S_n$-equivariant Convolutional Quantum Alternating Ans\"atze ($S_n$-CQA) using Young-Jucys-Murphy (YJM) elements. We prove that $S_n$-CQA is able to generate any unitary in any given $S_n$ irrep sector, which may serve as a universal model for a wide array of quantum machine learning problems with the presence of SU($d$) symmetry. Our method provides another way to prove the universality of Quantum Approximate Optimization Algorithm (QAOA) and verifies that 4-local SU($d$) symmetric unitaries are sufficient to build generic SU($d$) symmetric quantum circuits up to relative phase factors. We present numerical simulations to showcase the effectiveness of the ans\"atze to find the ground state energy of the $J_1$--$J_2$ antiferromagnetic Heisenberg model on the rectangular and Kagome lattices. Our work provides the first application of the celebrated Okounkov-Vershik's $S_n$ representation theory to quantum physics and machine learning, from which to propose quantum variational ans\"atze that strongly suggests to be classically intractable tailored towards a specific optimization problem.

Accurately labeling biomedical data presents a challenge. Traditional semi-supervised learning methods often under-utilize available unlabeled data. To address this, we propose a novel reliability-based training data cleaning method employing inductive conformal prediction (ICP). This method capitalizes on a small set of accurately labeled training data and leverages ICP-calculated reliability metrics to rectify mislabeled data and outliers within vast quantities of noisy training data. The efficacy of the method is validated across three classification tasks within distinct modalities: filtering drug-induced-liver-injury (DILI) literature with title and abstract, predicting ICU admission of COVID-19 patients through CT radiomics and electronic health records, and subtyping breast cancer using RNA-sequencing data. Varying levels of noise to the training labels were introduced through label permutation. Results show significant enhancements in classification performance: accuracy enhancement in 86 out of 96 DILI experiments (up to 11.4%), AUROC and AUPRC enhancements in all 48 COVID-19 experiments (up to 23.8% and 69.8%), and accuracy and macro-average F1 score improvements in 47 out of 48 RNA-sequencing experiments (up to 74.6% and 89.0%). Our method offers the potential to substantially boost classification performance in multi-modal biomedical machine learning tasks. Importantly, it accomplishes this without necessitating an excessive volume of meticulously curated training data.  ( 3 min )

In many randomized experiments, the treatment effect of the long-term metric (i.e. the primary outcome of interest) is often difficult or infeasible to measure. Such long-term metrics are often slow to react to changes and sufficiently noisy they are challenging to faithfully estimate in short-horizon experiments. A common alternative is to measure several short-term proxy metrics in the hope they closely track the long-term metric -- so they can be used to effectively guide decision-making in the near-term. We introduce a new statistical framework to both define and construct an optimal proxy metric for use in a homogeneous population of randomized experiments. Our procedure first reduces the construction of an optimal proxy metric in a given experiment to a portfolio optimization problem which depends on the true latent treatment effects and noise level of experiment under consideration. We then denoise the observed treatment effects of the long-term metric and a set of proxies in a historical corpus of randomized experiments to extract estimates of the latent treatment effects for use in the optimization problem. One key insight derived from our approach is that the optimal proxy metric for a given experiment is not apriori fixed; rather it should depend on the sample size (or effective noise level) of the randomized experiment for which it is deployed. To instantiate and evaluate our framework, we employ our methodology in a large corpus of randomized experiments from an industrial recommendation system and construct proxy metrics that perform favorably relative to several baselines.  ( 2 min )

In the presence of heterogeneous data, where randomly rotated objects fall into multiple underlying categories, it is challenging to simultaneously classify them into clusters and synchronize them based on pairwise relations. This gives rise to the joint problem of community detection and synchronization. We propose a series of semidefinite relaxations, and prove their exact recovery when extending the celebrated stochastic block model to this new setting where both rotations and cluster identities are to be determined. Numerical experiments demonstrate the efficacy of our proposed algorithms and confirm our theoretical result which indicates a sharp phase transition for exact recovery.  ( 2 min )

We introduce beta diffusion, a novel generative modeling method that integrates demasking and denoising to generate data within bounded ranges. Using scaled and shifted beta distributions, beta diffusion utilizes multiplicative transitions over time to create both forward and reverse diffusion processes, maintaining beta distributions in both the forward marginals and the reverse conditionals, given the data at any point in time. Unlike traditional diffusion-based generative models relying on additive Gaussian noise and reweighted evidence lower bounds (ELBOs), beta diffusion is multiplicative and optimized with KL-divergence upper bounds (KLUBs) derived from the convexity of the KL divergence. We demonstrate that the proposed KLUBs are more effective for optimizing beta diffusion compared to negative ELBOs, which can also be derived as the KLUBs of the same KL divergence with its two arguments swapped. The loss function of beta diffusion, expressed in terms of Bregman divergence, further supports the efficacy of KLUBs for optimization. Experimental results on both synthetic data and natural images demonstrate the unique capabilities of beta diffusion in generative modeling of range-bounded data and validate the effectiveness of KLUBs in optimizing diffusion models, thereby making them valuable additions to the family of diffusion-based generative models and the optimization techniques used to train them.  ( 2 min )

Large language models (LLMs) have played a pivotal role in revolutionizing various facets of our daily existence. Solving attention regression is a fundamental task in optimizing LLMs. In this work, we focus on giving a provable guarantee for the one-layer attention network objective function $L(X,Y) = \sum_{j_0 = 1}^n \sum_{i_0 = 1}^d ( \langle \langle \exp( \mathsf{A}_{j_0} x ) , {\bf 1}_n \rangle^{-1} \exp( \mathsf{A}_{j_0} x ), A_{3} Y_{*,i_0} \rangle - b_{j_0,i_0} )^2$. Here $\mathsf{A} \in \mathbb{R}^{n^2 \times d^2}$ is Kronecker product between $A_1 \in \mathbb{R}^{n \times d}$ and $A_2 \in \mathbb{R}^{n \times d}$. $A_3$ is a matrix in $\mathbb{R}^{n \times d}$, $\mathsf{A}_{j_0} \in \mathbb{R}^{n \times d^2}$ is the $j_0$-th block of $\mathsf{A}$. The $X, Y \in \mathbb{R}^{d \times d}$ are variables we want to learn. $B \in \mathbb{R}^{n \times d}$ and $b_{j_0,i_0} \in \mathbb{R}$ is one entry at $j_0$-th row and $i_0$-th column of $B$, $Y_{*,i_0} \in \mathbb{R}^d$ is the $i_0$-column vector of $Y$, and $x \in \mathbb{R}^{d^2}$ is the vectorization of $X$. In a multi-layer LLM network, the matrix $B \in \mathbb{R}^{n \times d}$ can be viewed as the output of a layer, and $A_1= A_2 = A_3 \in \mathbb{R}^{n \times d}$ can be viewed as the input of a layer. The matrix version of $x$ can be viewed as $QK^\top$ and $Y$ can be viewed as $V$. We provide an iterative greedy algorithm to train loss function $L(X,Y)$ up $\epsilon$ that runs in $\widetilde{O}( ({\cal T}_{\mathrm{mat}}(n,n,d) + {\cal T}_{\mathrm{mat}}(n,d,d) + d^{2\omega}) \log(1/\epsilon) )$ time. Here ${\cal T}_{\mathrm{mat}}(a,b,c)$ denotes the time of multiplying $a \times b$ matrix another $b \times c$ matrix, and $\omega\approx 2.37$ denotes the exponent of matrix multiplication.  ( 3 min )

We study policy evaluation of offline contextual bandits subject to unobserved confounders. Sensitivity analysis methods are commonly used to estimate the policy value under the worst-case confounding over a given uncertainty set. However, existing work often resorts to some coarse relaxation of the uncertainty set for the sake of tractability, leading to overly conservative estimation of the policy value. In this paper, we propose a general estimator that provides a sharp lower bound of the policy value. It can be shown that our estimator contains the recently proposed sharp estimator by Dorn and Guo (2022) as a special case, and our method enables a novel extension of the classical marginal sensitivity model using f-divergence. To construct our estimator, we leverage the kernel method to obtain a tractable approximation to the conditional moment constraints, which traditional non-sharp estimators failed to take into account. In the theoretical analysis, we provide a condition for the choice of the kernel which guarantees no specification error that biases the lower bound estimation. Furthermore, we provide consistency guarantees of policy evaluation and learning. In the experiments with synthetic and real-world data, we demonstrate the effectiveness of the proposed method.  ( 2 min )

Variational algorithms require architectures that naturally constrain the optimisation space to run efficiently. In geometric quantum machine learning, one achieves this by encoding group structure into parameterised quantum circuits to include the symmetries of a problem as an inductive bias. However, constructing such circuits is challenging as a concrete guiding principle has yet to emerge. In this paper, we propose the use of spin networks, a form of directed tensor network invariant under a group transformation, to devise SU(2) equivariant quantum circuit ans\"atze -- circuits possessing spin rotation symmetry. By changing to the basis that block diagonalises SU(2) group action, these networks provide a natural building block for constructing parameterised equivariant quantum circuits. We prove that our construction is mathematically equivalent to other known constructions, such as those based on twirling and generalised permutations, but more direct to implement on quantum hardware. The efficacy of our constructed circuits is tested by solving the ground state problem of SU(2) symmetric Heisenberg models on the one-dimensional triangular lattice and on the Kagome lattice. Our results highlight that our equivariant circuits boost the performance of quantum variational algorithms, indicating broader applicability to other real-world problems.  ( 2 min )

We aim to provide a general framework of for computational photography that recovers the real scene from imperfect images, via the Deep Nonparametric Convexified Filtering (DNCF). It is consists of a nonparametric deep network to resemble the physical equations behind the image formation, such as denoising, super-resolution, inpainting, and flash. DNCF has no parameterization dependent on training data, therefore has a strong generalization and robustness to adversarial image manipulation. During inference, we also encourage the network parameters to be nonnegative and create a bi-convex function on the input and parameters, and this adapts to second-order optimization algorithms with insufficient running time, having 10X acceleration over Deep Image Prior. With these tools, we empirically verify its capability to defend image classification deep networks against adversary attack algorithms in real-time.  ( 2 min )

We consider nonconvex-concave minimax problems, $\min_{\mathbf{x}} \max_{\mathbf{y} \in \mathcal{Y}} f(\mathbf{x}, \mathbf{y})$, where $f$ is nonconvex in $\mathbf{x}$ but concave in $\mathbf{y}$ and $\mathcal{Y}$ is a convex and bounded set. One of the most popular algorithms for solving this problem is the celebrated gradient descent ascent (GDA) algorithm, which has been widely used in machine learning, control theory and economics. Despite the extensive convergence results for the convex-concave setting, GDA with equal stepsize can converge to limit cycles or even diverge in a general setting. In this paper, we present the complexity results on two-time-scale GDA for solving nonconvex-concave minimax problems, showing that the algorithm can find a stationary point of the function $\Phi(\cdot) := \max_{\mathbf{y} \in \mathcal{Y}} f(\cdot, \mathbf{y})$ efficiently. To the best our knowledge, this is the first nonasymptotic analysis for two-time-scale GDA in this setting, shedding light on its superior practical performance in training generative adversarial networks (GANs) and other real applications.  ( 3 min )

We introduce a new debiasing framework for high-dimensional linear regression that bypasses the restrictions on covariate distributions imposed by modern debiasing technology. We study the prevalent setting where the number of features and samples are both large and comparable. In this context, state-of-the-art debiasing technology uses a degrees-of-freedom correction to remove shrinkage bias of regularized estimators and conduct inference. However, this method requires that the observed samples are i.i.d., the covariates follow a mean zero Gaussian distribution, and reliable covariance matrix estimates for observed features are available. This approach struggles when (i) covariates are non-Gaussian with heavy tails or asymmetric distributions, (ii) rows of the design exhibit heterogeneity or dependencies, and (iii) reliable feature covariance estimates are lacking. To address these, we develop a new strategy where the debiasing correction is a rescaled gradient descent step (suitably initialized) with step size determined by the spectrum of the sample covariance matrix. Unlike prior work, we assume that eigenvectors of this matrix are uniform draws from the orthogonal group. We show this assumption remains valid in diverse situations where traditional debiasing fails, including designs with complex row-column dependencies, heavy tails, asymmetric properties, and latent low-rank structures. We establish asymptotic normality of our proposed estimator (centered and scaled) under various convergence notions. Moreover, we develop a consistent estimator for its asymptotic variance. Lastly, we introduce a debiased Principal Component Regression (PCR) technique using our Spectrum-Aware approach. In varied simulations and real data experiments, we observe that our method outperforms degrees-of-freedom debiasing by a margin.  ( 3 min )

In the presence of right-censored data with covariates, the conditional Kaplan-Meier estimator (also known as the Beran estimator) consistently estimates the conditional survival function of the random follow-up for the event of interest. However, a necessary condition is the unambiguous knowledge of whether each individual is censored or not, which may be incomplete in practice. We therefore propose a study of the Beran estimator when the censoring indicators are generic random variables and discuss necessary conditions for the efficiency of the Beran estimator. From this, we provide a new estimator for the conditional survival function with missing not at random (MNAR) censoring indicators based on a conditional copula model for the missingness mechanism. In addition to the theoretical results, we illustrate how the estimators work for small samples through a simulation study and show their practical applicability by analyzing synthetic and real data.  ( 2 min )

We consider the problem of approximating the regression function from noisy vector-valued data by an online learning algorithm using an appropriate reproducing kernel Hilbert space (RKHS) as prior. In an online algorithm, i.i.d. samples become available one by one by a random process and are successively processed to build approximations to the regression function. We are interested in the asymptotic performance of such online approximation algorithms and show that the expected squared error in the RKHS norm can be bounded by $C^2 (m+1)^{-s/(2+s)}$, where $m$ is the current number of processed data, the parameter $0<s\leq 1$ expresses an additional smoothness assumption on the regression function and the constant $C$ depends on the variance of the input noise, the smoothness of the regression function and further parameters of the algorithm.  ( 2 min )

Several fundamental problems in science and engineering consist of global optimization tasks involving unknown high-dimensional (black-box) functions that map a set of controllable variables to the outcomes of an expensive experiment. Bayesian Optimization (BO) techniques are known to be effective in tackling global optimization problems using a relatively small number objective function evaluations, but their performance suffers when dealing with high-dimensional outputs. To overcome the major challenge of dimensionality, here we propose a deep learning framework for BO and sequential decision making based on bootstrapped ensembles of neural architectures with randomized priors. Using appropriate architecture choices, we show that the proposed framework can approximate functional relationships between design variables and quantities of interest, even in cases where the latter take values in high-dimensional vector spaces or even infinite-dimensional function spaces. In the context of BO, we augmented the proposed probabilistic surrogates with re-parameterized Monte Carlo approximations of multiple-point (parallel) acquisition functions, as well as methodological extensions for accommodating black-box constraints and multi-fidelity information sources. We test the proposed framework against state-of-the-art methods for BO and demonstrate superior performance across several challenging tasks with high-dimensional outputs, including a constrained multi-fidelity optimization task involving shape optimization of rotor blades in turbo-machinery.  ( 3 min )

Benign overfitting, the phenomenon where interpolating models generalize well in the presence of noisy data, was first observed in neural network models trained with gradient descent. To better understand this empirical observation, we consider the generalization error of two-layer neural networks trained to interpolation by gradient descent on the logistic loss following random initialization. We assume the data comes from well-separated class-conditional log-concave distributions and allow for a constant fraction of the training labels to be corrupted by an adversary. We show that in this setting, neural networks exhibit benign overfitting: they can be driven to zero training error, perfectly fitting any noisy training labels, and simultaneously achieve minimax optimal test error. In contrast to previous work on benign overfitting that require linear or kernel-based predictors, our analysis holds in a setting where both the model and learning dynamics are fundamentally nonlinear.  ( 2 min )

Variable importance assessment has become a crucial step in machine-learning applications when using complex learners, such as deep neural networks, on large-scale data. Removal-based importance assessment is currently the reference approach, particularly when statistical guarantees are sought to justify variable inclusion. It is often implemented with variable permutation schemes. On the flip side, these approaches risk misidentifying unimportant variables as important in the presence of correlations among covariates. Here we develop a systematic approach for studying Conditional Permutation Importance (CPI) that is model agnostic and computationally lean, as well as reusable benchmarks of state-of-the-art variable importance estimators. We show theoretically and empirically that $\textit{CPI}$ overcomes the limitations of standard permutation importance by providing accurate type-I error control. When used with a deep neural network, $\textit{CPI}$ consistently showed top accuracy across benchmarks. An empirical benchmark on real-world data analysis in a large-scale medical dataset showed that $\textit{CPI}$ provides a more parsimonious selection of statistically significant variables. Our results suggest that $\textit{CPI}$ can be readily used as drop-in replacement for permutation-based methods.  ( 2 min )

The swift progression of machine learning (ML) has not gone unnoticed in the realm of statistical mechanics. ML techniques have attracted attention by the classical density-functional theory (DFT) community, as they enable discovery of free-energy functionals to determine the equilibrium-density profile of a many-particle system. Within DFT, the external potential accounts for the interaction of the many-particle system with an external field, thus, affecting the density distribution. In this context, we introduce a statistical-learning framework to infer the external potential exerted on a many-particle system. We combine a Bayesian inference approach with the classical DFT apparatus to reconstruct the external potential, yielding a probabilistic description of the external potential functional form with inherent uncertainty quantification. Our framework is exemplified with a grand-canonical one-dimensional particle ensemble with excluded volume interactions in a confined geometry. The required training dataset is generated using a Monte Carlo (MC) simulation where the external potential is applied to the grand-canonical ensemble. The resulting particle coordinates from the MC simulation are fed into the learning framework to uncover the external potential. This eventually allows us to compute the equilibrium density profile of the system by using the tools of DFT. Our approach benchmarks the inferred density against the exact one calculated through the DFT formulation with the true external potential. The proposed Bayesian procedure accurately infers the external potential and the density profile. We also highlight the external-potential uncertainty quantification conditioned on the amount of available simulated data. The seemingly simple case study introduced in this work might serve as a prototype for studying a wide variety of applications, including adsorption and capillarity.  ( 3 min )

Artificial intelligence models and methods commonly lack causal interpretability. Despite the advancements in interpretable machine learning (IML) methods, they frequently assign importance to features which lack causal influence on the outcome variable. Selecting causally relevant features among those identified as relevant by these methods, or even before model training, would offer a solution. Feature selection methods utilizing information theoretical quantities have been successful in identifying statistically relevant features. However, the information theoretical quantities they are based on do not incorporate causality, rendering them unsuitable for such scenarios. To address this challenge, this article proposes information theoretical quantities that incorporate the causal structure of the system, which can be used to evaluate causal importance of features for some given outcome variable. Specifically, we introduce causal versions of entropy and mutual information, termed causal entropy and causal information gain, which are designed to assess how much control a feature provides over the outcome variable. These newly defined quantities capture changes in the entropy of a variable resulting from interventions on other variables. Fundamental results connecting these quantities to the existence of causal effects are derived. The use of causal information gain in feature selection is demonstrated, highlighting its superiority over standard mutual information in revealing which features provide control over a chosen outcome variable. Our investigation paves the way for the development of methods with improved interpretability in domains involving causation.  ( 3 min )

Gaussian process is an indispensable tool in clustering functional data, owing to it's flexibility and inherent uncertainty quantification. However, when the functional data is observed over a large grid (say, of length $p$), Gaussian process clustering quickly renders itself infeasible, incurring $O(p^2)$ space complexity and $O(p^3)$ time complexity per iteration; and thus prohibiting it's natural adaptation to large environmental applications. To ensure scalability of Gaussian process clustering in such applications, we propose to embed the popular Vecchia approximation for Gaussian processes at the heart of the clustering task, provide crucial theoretical insights towards algorithmic design, and finally develop a computationally efficient expectation maximization (EM) algorithm. Empirical evidence of the utility of our proposal is provided via simulations and analysis of polar temperature anomaly (\href{https://www.ncei.noaa.gov/access/monitoring/climate-at-a-glance/global/time-series}{noaa.gov}) data-sets.  ( 2 min )

In consequential domains, it is often impossible to compel individuals to take treatment, so that optimal policy rules are merely suggestions in the presence of human non-adherence to treatment recommendations. In these same domains, there may be heterogeneity both in who responds in taking-up treatment, and heterogeneity in treatment efficacy. While optimal treatment rules can maximize causal outcomes across the population, access parity constraints or other fairness considerations can be relevant in the case of encouragement. For example, in social services, a persistent puzzle is the gap in take-up of beneficial services among those who may benefit from them the most. When in addition the decision-maker has distributional preferences over both access and average outcomes, the optimal decision rule changes. We study causal identification, statistical variance-reduced estimation, and robust estimation of optimal treatment rules, including under potential violations of positivity. We consider fairness constraints such as demographic parity in treatment take-up, and other constraints, via constrained optimization. Our framework can be extended to handle algorithmic recommendations under an often-reasonable covariate-conditional exclusion restriction, using our robustness checks for lack of positivity in the recommendation. We develop a two-stage algorithm for solving over parametrized policy classes under general constraints to obtain variance-sensitive regret bounds. We illustrate the methods in two case studies based on data from randomized encouragement to enroll in insurance and from pretrial supervised release with electronic monitoring.  ( 2 min )

The recipe behind the success of deep learning has been the combination of neural networks and gradient-based optimization. Understanding the behavior of gradient descent however, and particularly its instability, has lagged behind its empirical success. To add to the theoretical tools available to study gradient descent we propose the principal flow (PF), a continuous time flow that approximates gradient descent dynamics. To our knowledge, the PF is the only continuous flow that captures the divergent and oscillatory behaviors of gradient descent, including escaping local minima and saddle points. Through its dependence on the eigendecomposition of the Hessian the PF sheds light on the recently observed edge of stability phenomena in deep learning. Using our new understanding of instability we propose a learning rate adaptation method which enables us to control the trade-off between training stability and test set evaluation performance.  ( 2 min )

Google has developed an AI algorithm to refine route suggestions on Google Maps, personalizing it based on user data and behavior, allegedly improving the accuracy on an average by 16-24 percent. To stay on top of the latest advancements in AI, look here first. https://preview.redd.it/rp21ylzo7iob1.jpg?width=770&format=pjpg&auto=webp&s=737cf3fd278ade42900e8586d626a7c729522c1a Personalized Route Suggestions through AI The AI model comprises 360 million parameters, using real-time data from Maps users to influence factors including travel time, road conditions, tolls, and personal preferences to suggest routes. This technology is grounded on "inverse reinforcement learning" (IRL), specifically a new IRL algorithm - "Receding Horizon Inverse Planning (RHIP)". The Power of RHIP and AI in Maps Google and Deepmind jointly worked to develop RHIP, using complex stochastic models in immediate vicinity areas, but switching to simpler deterministic methods for distant areas for power conservation. The AI improves route suggestions for both driving and two-wheeled vehicles by learning from Maps users' movements and behaviors over time. Google states that this is the largest application of inverse reinforcement learning for route planning to date. Implementation and User Testing Google has applied the algorithm to Maps data globally, but extensive user testing is needed to confirm if the technique consistently produces better routes. Previous attempts at using AI systems for route planning on a large scale have often failed due to the complexity of road networks. (source) P.S. If you like this kind of analysis, I write a free newsletter that tracks the most relevant news and research in AI. Professionals from Google, Meta, and OpenAI are already reading it. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

There are a number of reasons why some Republicans may be in denial about Trump's role in the January 6th Capitol riot. Trump has been very effective at cultivating a cult of personality among his supporters. These supporters are often fiercely loyal to him and willing to believe whatever he says, even if it contradicts reality. Many Republicans have been reluctant to criticize Trump for fear of alienating his supporters. This is especially true for Republican politicians who are up for re-election in 2022. Some Republicans may genuinely believe that Trump did not incite the riot. They may point to the fact that he did not explicitly call for violence, or that he told his supporters to "peacefully and patriotically make your voices heard." Other Republicans may be motivated by partis…  ( 10 min )

Factory farming is a cruel and unsustainable way to produce meat. Animals are crammed into small, filthy spaces, where they are often denied their basic needs. They are given antibiotics to prevent disease and growth hormones to make them grow faster. This leads to a variety of health problems for the animals, as well as for the people who eat their meat. Clean meat is a new technology that offers a more humane and sustainable way to produce meat. It is made from animal cells that are grown in a lab, without the need for animals to be raised or slaughtered. Clean meat is still in its early stages of development, but it has the potential to revolutionize the meat industry. Here are some of the reasons why factory farming must be replaced by clean meat: It is cruel to animals. Factory f…  ( 10 min )

News provided by aibrews.com Stability AI launched Stable Audio, a generative AI tool for music & sound generation from text. The underlying latent diffusion model architecture uses audio conditioned on text metadata as well as audio file duration and start time [Details]. Coqui released XTTS - a new voice generation model that lets you clone voices in 13 different languages by using just a quick 3-second audio clip [Details]. Microsoft Research released and open-sourced Phi-1.5 - a 1.3 billion parameter transformer-based model with performance on natural language tasks comparable to models 5x larger [Paper ]. Project Gutenberg, Microsoft and MIT have worked together to use neural text-to-speech to create and release thousands of human-quality free and open audiobooks [Details]. Res…  ( 11 min )

Has anyone dropped an opensource tool like run.ai, to leverage multiple gpus / distribute the workload a bit more efficiently? I'm loving some of the single gpu llm modifications that have been dropping recently (have a couple i've tested that ran well on 4090 and 3090ti in the lab), but i've got a plethora of 8 & 12 gig 3xxx series cards i'd love to take advantage of beyond passthroughs to individual vms. Looking for any solutions. Speed isn't as important as the ability to distributively run larger models. submitted by /u/SwallowedBuckyBalls [link] [comments]  ( 9 min )

submitted by /u/glenniszen [link] [comments]  ( 8 min )

I am looking for a free AI tool, site (may be paid with free account limited options) that can do a summary of English article (like BBC, youtube) based on most frequently used English words, for example Oxford list of 3000 most frequently used words in English. In other words if word is not in that list it should replace the word with the one that is on the list or its definition. Pdf submitted by /u/vibrantform [link] [comments]  ( 9 min )

I said to Bard: It seems like these are difficult times. Compare today with the WWII years, explaining how much we have to be grateful for. Bard said: I agree that these are difficult times. The world is facing many challenges, including the COVID-19 pandemic, climate change, and political instability. However, I believe that we have much to be grateful for today compared to the WWII years. Here are just a few things: We live in a time of peace. World War II was a global conflict that caused the deaths of millions of people. Today, there are no major wars raging on. We have access to advanced medical care. During WWII, many people died from diseases that are now easily treatable. Today, we have antibiotics, vaccines, and other medical advances that have saved countless lives. We have a higher standard of living. In the WWII years, many people lived in poverty. Today, most people in the developed world have access to clean water, food, and shelter. We have more opportunities for education and advancement. In the WWII years, many people were denied an education because of their race, gender, or social class. Today, education is more accessible than ever before, and people from all backgrounds have the opportunity to succeed. We have a better understanding of the world. During WWII, there was a lot of misinformation and propaganda. Today, we have access to more information than ever before, and we can make more informed decisions about our lives. Of course, there are still many challenges that we face today. But I believe that we have come a long way since the WWII years, and we have much to be grateful for. Let us never forget the sacrifices that were made during that time, and let us continue to work towards a better future for all. submitted by /u/Georgeo57 [link] [comments]  ( 9 min )

The AI-based techno-economic war between the West and China will determine the global distribution of technology, economic benefits, and influence. The winner of this race, particularly in AI, will have the power to accelerate GDP growth and project global economic benefits. China is actively pursuing technological leadership in AI and 5G wireless, while the West must prioritize technological talent influx and leverage its venture and technology ecosystem. Aggressive AI regulation could hinder the West's momentum in this race. The risks of this war and AI technology are significant, but it is a greater risk to let adversaries have unconstrained power. Global treaties are not enforceable solutions to regulate AI. Source : https://medium.com/@vkhosla/what-is-at-stake-in-this-ai-based-techno-economic-war-between-the-west-and-china-8f76bd291be7 submitted by /u/NuseAI [link] [comments]  ( 9 min )

There's no need to specify which product fulfills your needs, but we'd like everyone to focus on describing their current frustrations and the issues they urgently hope AI can help them with. submitted by /u/Minna_Z [link] [comments]  ( 9 min )

submitted by /u/Agitated-Spell3979 [link] [comments]  ( 8 min )

Hello everyone! I'm embarking on a project to create an AI-model hub—a platform where users can upload and utilize their AI models. While I'm aware of popular platforms that offer this, my primary goal is for educational purposes. I'd greatly appreciate any recommendations for helpful articles, videos, or codebases to guide me on this journey. Thanks in advance! submitted by /u/Electronic-Choice-86 [link] [comments]  ( 9 min )

Hello everyone, Introducing LLMa: ChatGPT built around YOU (getllma.com) - a dedicated service offering hands-on expertise to integrate state-of-the-art generative AI tailored for your projects. We utilize open-source models and train them to outperform GPT-4 on tasks specific to your domain. Envision having a seasoned AI specialist on your team, ensuring your model not only rivals the big players but excels in your unique challenges. 🌟 Why LLMa? Personalized Expertise: Our team collaborates closely with you, delving into your needs and sculpting a model that thrives in your domain. Bespoke Training: We refine open-source models (LLaMa, T5, etc.) with plenty of secret tricks to specialize and surpass GPT-4's performance for your specific tasks. Cost-Effective: LLMa tends to be around 100x cheaper than GPT-4, offering significant savings. No recurring fees; invest in a one-time fee based on your model's complexity. Full Ownership: We hand over the model files/weights to you. It's entirely yours, ensuring total privacy with no PII leaks. Deployment Assistance: Beyond just crafting the model, we can guide you in deploying it, ensuring a seamless integration into your operations. Ongoing Support: From initial brainstorming to model deployment, we're with you, ensuring success at every phase. 💼 Tailored for Enterprises: LLMa is meticulously crafted for enterprises that aim for a high-performing, bespoke AI solution. Transparent pricing begins at $500, contingent on your distinct requirements. ❓ Navigating the Generative AI Terrain? Embarking on the vast journey of generative AI? LLMa is your compass. We aid in defining challenges, strategizing solutions, and optimizing the AI potential for your endeavors. If LLMa piques your interest or if you have any queries, fill-in the form, drop a comment below or DM me. I'm all ears and eager to connect! submitted by /u/iliashark [link] [comments]  ( 9 min )

Quick template that bootstraps Amazon SageMaker running a LlaMa 2 model from Hugging Face. Everything deployed as code (Python), no manual tweaking in the SageMaker console. www.pulumi.com/blog/mlops-huggingface-llm-aws-sagemaker-python/ submitted by /u/kao-pulumi [link] [comments]  ( 9 min )

Am trying to train GANs for oversampling a minority text class (am feeding it only the minority class), but the results dont seem to improve much (AUC only improves by .03 so far). while basic oversampling techniques like SMOTE gives way better results. also am using a vector representation for the whole text instead of word embedding(same used for SMOTE), i tried different architectures with CNN. is there any tricks maybe in training the discriminator and generator ? i can't seem to find the problem submitted by /u/SlightSecretaryB [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2309.07870 Github: https://github.com/aiwaves-cn/agents Abstract: Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with environments, humans, and other agents using natural language interfaces. We consider language agents as a promising direction towards artificial general intelligence and release Agents, an open-source library with the goal of opening up these advances to a wider non-specialist audience. Agents is carefully engineered to support important features including planning, memory, tool usage, multi-agent communication, and fine-grained symbolic control. Agents is user-friendly as it enables non-specialists to build, customize, test, tune, and deploy state-of-the-art autonomous language agents without much coding. The library is also research-friendly as its modularized design makes it easily extensible for researchers. https://preview.redd.it/3bdi71r5rgob1.jpg?width=1131&format=pjpg&auto=webp&s=760942c19be6ecda791414c812a77e72751c526d https://preview.redd.it/howf64r5rgob1.jpg?width=1656&format=pjpg&auto=webp&s=636744fccab7a1c2bafb902bad5dbb647440fff5 ​ submitted by /u/Singularian2501 [link] [comments]  ( 9 min )

Hi all, I have about 100K images on my machine and I am looking for a tool that can help me do some QA on it. Example features I would love: Search and visualize all images with a prefix "cls1_" or "cls2_" Easily rename file names if they're not named correctly Visualize all captions associated with each image We can assume they have the same name but with extension ".txt" or ".captions" Or there's a metadata.json linking between "img_file" and "caption_file" Easily edit captions in the dashboard I can also work with some kind of metadata file instead of relying on filename logic if it really helps a certain tool. I prefer a locally run, open-source tool. It would be a problem for me to upload this data to any online platform. Many thanks in advance for any help or guidance. submitted by /u/JYP_Scouter [link] [comments]  ( 9 min )

Before training my model im going through multiple steps to collect and process my data. One of these steps to is calculate values from algorithmic and mathematical functions. In my training and testing data the values are around 12-15 decimal places. I then split the data without shuffling. Training, validation and testing averages at 75% accuracy. Now my next step I wanted to do a "real world" data test where I collect the exact same data as my testing data and predict it using my previously built model but the values inside test_dataset1 are different to test_dataset2 within the last 6-9 decimal places even though the data is going through the exact same code. For example Test_dataset1 Value : 1.123456789 Test_dataset2 Value : 1.123456987 This messes with my prediction and its making me wonder aswell as standrising my data should I be rounding my float values to say 4-6 decimal places? submitted by /u/paddockson [link] [comments]  ( 9 min )

What are the key differences between the Tensor Processing Unit (TPU) found in Google Tensor chips and the Neural Engine found in Apple's A and M series chips? Are they the same things? Or is the TPU only available for Google's own AI, while the Neural Engine is available to all developers for accelerating AI for all apps, if they decide to? Can developers optimize apps for Google Tensor like they can for the Neural Engine? If not, how do developers take advantage of machine learning acceleration chips on Google Pixel or Android in general? If yes, let's say a developer optimizes their app for the Google TPU, will they need to re-optimize for other chips like Samsung's NPU or Qualcomm AI too? If not, how well do they run? Are they the same fast and efficient? submitted by /u/GRguy_21 [link] [comments]  ( 9 min )

Working on Final year project in the field on computer vision: Image Dehazing. I aim at having a novel approch for better dehazing of face haze images. Have read papers related to single image dehazing & face SR/Deblur. Any thoughts/ suggestions/ leads would be appreciated. submitted by /u/GahlotB [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2309.07315 Abstract: Transformers have significantly advanced the field of natural language processing, but comprehending their internal mechanisms remains a challenge. In this paper, we introduce a novel geometric perspective that elucidates the inner mechanisms of transformer operations. Our primary contribution is illustrating how layer normalization confines the latent features to a hyper-sphere, subsequently enabling attention to mold the semantic representation of words on this surface. This geometric viewpoint seamlessly connects established properties such as iterative refinement and contextual embeddings. We validate our insights by probing a pre-trained 124M parameter GPT-2 model. Our findings reveal clear query-key attention patterns in early layers and build upon prior observations regarding the subject-specific nature of attention heads at deeper layers. Harnessing these geometric insights, we present an intuitive understanding of transformers, depicting them as processes that model the trajectory of word particles along the hyper-sphere. ​ https://preview.redd.it/0i302t857gob1.png?width=1864&format=png&auto=webp&s=1da999c014979bdb6c99809d5b38eb5ccfd717d0 submitted by /u/CoolThingsOnTop [link] [comments]  ( 9 min )

Repo -> https://github.com/MichaelisTrofficus/hampel_filter The Python library hampel implements the Hampel Filter, which is generally used to detect anomalies in data with a timeseries structure. It basically consists of a sliding window of a parameterizable size. The library was in plain Python before (using pandas for all the sliding operations, median computation etc), but now it has been replaced by a Cython implementation, which speeds up things quite a bit! 😀 It also provides much more valuable information (thresholds, median absolute deviations, etc.), allowing us to create plots like this one: https://preview.redd.it/6j4ubiwgmfob1.png?width=800&format=png&auto=webp&s=bbc56777fce30a464d0bb33ac5126033b3413838 ​ submitted by /u/Hefty-Consequence443 [link] [comments]  ( 9 min )

Hi everybody. Currently, I am building a Deep Learning model with the task of automatically generating random posts and tweets. The characteristic is that it must have the personality of the writer, for example the user is the CEO of company A, then the generated post must have the writing style of the CEO or company A, similar to other users. Actually, I don't know where to start solving this problem. I intend to use RWKV to do this problem but I'm not sure if it is a good direction or not. Is there any related research or can anyone who has done this problem give me some suggestions? submitted by /u/unknow_from_vietnam [link] [comments]  ( 9 min )

Paper. I am not affiliated with this work or its authors. Abstract: Transformers have become the dominant model in deep learning, but the reason for their superior performance is poorly understood. Here, we hypothesize that the strong performance of Transformers stems from an architectural bias towards mesa-optimization, a learned process running within the forward pass of a model consisting of the following two steps: (i) the construction of an internal learning objective, and (ii) its corresponding solution found through optimization. To test this hypothesis, we reverse-engineer a series of autoregressive Transformers trained on simple sequence modeling tasks, uncovering underlying gradient-based mesa-optimization algorithms driving the generation of predictions. Moreover, we show that the learned forward-pass optimization algorithm can be immediately repurposed to solve supervised few-shot tasks, suggesting that mesa-optimization might underlie the in-context learning capabilities of large language models. Finally, we propose a novel self-attention layer, the mesa-layer, that explicitly and efficiently solves optimization problems specified in context. We find that this layer can lead to improved performance in synthetic and preliminary language modeling experiments, adding weight to our hypothesis that mesa-optimization is an important operation hidden within the weights of trained Transformers. Twitter thread about the paper from one of the paper's authors. Nitter thread, for those who aren't signed into Twitter but want to see the entire Twitter thread. Background info: Mesa-Optimization: Explain it like I'm 10 Edition. submitted by /u/Wiskkey [link] [comments]  ( 9 min )

Relevant Paper: 2307.08621.pdf (arxiv.org) So the definition of the recurrent representation of the retention mechanism is below Sn = γSn−1 + K⊺nVn Retention(Xn) = QnSn, n = 1, · · · , |x| γ is a decay factor, and K, Q, and V have their standard transformer definitions. What confuses me is the derivation of Sn. The formula makes it look like a scalar. But if that's the case, are we saying that for a given token, the retention mechanism is just multiplying the Query by a scalar? That's surprising! How is that able to provide enough context? Here is some code I wrote with GPT to show my understanding of how it works. Is this correct? I use 3 arbitrary tokens of dimension 3, and then a pick arbitrary K Q and V matrices. I also initialize gamma to 0.5 import numpy as np # Tokens x1…  ( 10 min )

Help required from NLP and Text Researchers !! Hello everyone! I have Urdu language transcriptions (text) which contain many misspelled words that are not part of the Urdu language. I wanted to know do we have any good NLP techniques or methods which can solve this problem for Urdu language? I want to replace these misspelled words with the correct words in Urdu. I have already tried Python libraries and methods such as indic-nlp, Levenshtein distance, UrduHack, Word2vec Urdu etc, but they weren't able to solve this problem. Some of the methods require Urdu dictionaries to find the correct word, which I'm also unable to find open-source on internet (please also help in that if possible). Will appreciate everyone's help and response to this. Thank you! submitted by /u/a_r182 [link] [comments]  ( 9 min )

Hi everyone! I am a (soon graduating) MSc student at a top European university and I'd like to apply for a ML PhD in the US this Fall. I've done my research on schools and advisors, but I figured there's no harm in also asking in this subreddit. What are some groups/professors that do ML research at US unis in deep learning theory (specifically foundations) and optimization? As an example, I'm talking about topics such as: https://arxiv.org/abs/1902.08129, https://arxiv.org/abs/1711.04735, https://arxiv.org/abs/2306.04637. Thank you all! Cheers! submitted by /u/AlexIsEpic24 [link] [comments]  ( 9 min )

Help required from NLP and Text Researchers !! Hello everyone! I have Urdu language transcriptions (text) which contain many misspelled words that are not part of the Urdu language. I wanted to know do we have any good NLP techniques or methods which can solve this problem for Urdu language? I want to replace these misspelled words with the correct words in Urdu. I have already tried Python libraries and methods such as indic-nlp, Levenshtein distance, UrduHack, Word2vec Urdu etc, but they weren't able to solve this problem. Some of the methods require Urdu dictionaries to find the correct word, which I'm also unable to find open-source on internet (please also help in that if possible). Will appreciate everyone's help and response to this. Thank you! submitted by /u/a_r182 [link] [comments]  ( 9 min )

So I’m starting my masters thesis project in ML ASAP and need a research topic. What areas (if any) are currently hot / feasible to do research in roughly 6 months with fairly limited compute access? I’m more interested in theory / research heavy areas rather than applied. And probably happier to dig into some hard math rather than taking on a software engineering type project. Any thoughts or general feedback very welcome! Thanks! submitted by /u/Professional-Pace158 [link] [comments]  ( 9 min )

Just came across this property of matrices that I have never seen before as I am contributing to the NumPy codebase and someone asked for this feature to be added and it got me thinking. It is defined as: A skew-symmetric matrix is a square matrix whose transpose equals to its negative. It should satisfy the below condition: AT = –A Just wondering do these have any applications in ML at all? I never came across this in my math classes so just wondering if the property pops up anywhere else in the field. Maybe in 3D space applications? Or maybe RBG data augmentation? But yeah not 100% sure. submitted by /u/Ok_Reality2341 [link] [comments]  ( 9 min )

I’m an electrical engineering student in college currently, and have been learning about ML for a few months now. I will be starting a ML research paper under my professor from next week. However, my DSA skills are quite sub-par. Should I focus more on ML math and projects, or take sufficient time out for leetcode as well? submitted by /u/4R1N1493 [link] [comments]  ( 9 min )

Hi, I have a dataset that consists of 2D trajectories and I am aiming to develop an autoencoder architecture to learn a compressed set of features that reasonable represents and can reconstruct the trajectories. The trajectories may look something like this as an example. A 2D image as input would seem to require a very sparse representation with high resolution to track the trajectory path. I am hoping there is a better way to input the path without requiring high resolution. An alternative might be to use a LSTM structure to input as a sequence, although not sure that solves the resolution issue. Do you have any suggestions? I've worked with 1d time series and 2D images just fine but this is a bit different. ​ ​ https://preview.redd.it/vqz8y3o69bob1.png?width=2020&format=png&auto=webp&s=d8bcc2fe311743c8e78a96055e68f1ad364b48c3 submitted by /u/ZeApelido [link] [comments]  ( 9 min )

If you ask someone a question and they say “yes” immediately, that gives you different information than if they pause and slowly say “yes.” The information you receive is not just the response but also the time it took to generate the response. Encryption can be analogous. The time it takes to encrypt data can […] Timing attacks first appeared on John D. Cook.  ( 5 min )

I concluded the previous post by saying elliptic curve Diffie-Hellman key exchange (ECDHE) requires smaller keys than finite field Diffie-Hellman (FFDHE) to obtain the same level of security. How much smaller are we talking about? According to NIST recommendations, a 256-bit elliptic curve curve provides about the same security as working over a 3072-bit finite […] Elliptic curve Diffie-Hellman key exchange first appeared on John D. Cook.  ( 6 min )

Diffie-Hellman key exchange is conceptually simple. Alice and Bob want to generate a shared cryptographic key. They want to use asymmetric (public) cryptography to share a symmetric (private) key. The starting point is a large prime p and a generator 1 < g < p. Alice generates a large random number x, her private key, […] Finite field Diffie Hellman primes first appeared on John D. Cook.  ( 6 min )

submitted by /u/keghn [link] [comments]  ( 8 min )

So I want to create a neural network from scratch, like no lib(tensorflow, pytorch, etc…), so what’s the best book for that, I know both calculus and statistics, so I’m assuming that the math wouldn’t be a problem. Also I will be using Cuda for its speed. submitted by /u/GateCodeMark [link] [comments]  ( 9 min )

https://news.accelerationrobotics.com/robotperf-beta/ Together with AMD, Intel, Ford, Harvard, Klagenfurt University, Georgia Institute of Technology, Boston University, Johannes Kepler University Linz, Barnard College, Columbia University and Carnegie Mellon University we are thrilled to introduce the beta release of RobotPerf™ Benchmarks, an advanced benchmarking suite crafted specifically to evaluate robotics computing performance using ROS 2 as its baseline. In this beta release, we not only showcase new benchmarks and results but also introduce novel visualization capabilities. The complete release is available at https://github.com/robotperf/benchmarks/releases/tag/beta. https://preview.redd.it/5whys5ufudob1.png?width=1562&format=png&auto=webp&s=08a6e22a0b07b26fa6340f59ec9df822ab49c9d0 submitted by /u/pablocarrera [link] [comments]  ( 9 min )

Hi, i just finished the coursera ml course and i wanted to create myself a double dqn model but my model don't seem to learn anything, it always return very low rewards (-100 to -300) even after playing 2000 episodes. I've been stuck on this for 4 days without any hope to find the solution, any help would be welcome :') thank you in advance import random import numpy as np import gymnasium as gym import tensorflow as tf from collections import deque, namedtuple from tensorflow.keras import Sequential, Input from tensorflow.keras.layers import Dense from tensorflow.keras.optimizers import Adam from tensorflow.keras.losses import MeanSquaredError import matplotlib.pyplot as plt # function creating the models def createModel(inputSize, outputSize): model = Sequential([ Input(inputSize),…  ( 10 min )

I'm confused with several algorithms that are based on an actor-critic approach. In TD3 and SAC, it is understandable that each of them is implemented to serve their purpose (deterministic and stachastic action). But in Dreamer algorithm (DreamerV3), why does it require to combine actor and critic network to the model-based planning approach, as the model-based also able to perform an action by planning to the simulation state. It is mean that using model-based to simulate the possible future then update the critic according to the simulation might sound good in training an agent? submitted by /u/AnnonymeowCat [link] [comments]  ( 9 min )

Posted by Haolin Jia, Software Engineer, and Qifei Wang, Senior Software Engineer, Core ML In recent years, we have witnessed rising interest across consumers and researchers in integrated augmented reality (AR) experiences using real-time face feature generation and editing functions in mobile applications, including short videos, virtual reality, and gaming. As a result, there is a growing demand for lightweight, yet high-quality face generation and editing models, which are often based on generative adversarial network (GAN) techniques. However, the majority of GAN models suffer from high computational complexity and the need for a large training dataset. In addition, it is also important to employ GAN models responsibly. In this post, we introduce MediaPipe FaceStylizer, an effi…  ( 92 min )

Large language model (LLM) agents are programs that extend the capabilities of standalone LLMs with 1) access to external tools (APIs, functions, webhooks, plugins, and so on), and 2) the ability to plan and execute tasks in a self-directed fashion. Often, LLMs need to interact with other software, databases, or APIs to accomplish complex tasks. […]  ( 13 min )

With Style2Fab, makers can rapidly customize models of 3D-printable objects, such as assistive devices, without hampering their functionality.  ( 10 min )

Please provide Arkiv links. If you want to share your thoughts then go for it. By new I mean within the last 6 months. submitted by /u/I_will_delete_myself [link] [comments]  ( 9 min )

What are some good papers to understand posterior collapse in VAEs? submitted by /u/randomkolmogorov [link] [comments]  ( 9 min )

Full Video Tutorial: https://www.linkedin.com/posts/moisesdias\_english-version-below-tutorial-crie-activity-7107686497885011969-ZLVW/ Hello everyone! Have you ever thought about how to create an object detection system using YOLO that works with any game? If you're interested, I've created a tutorial with all the steps to develop this system. I'll leave a link to the video where I demonstrate the process step by step using the game Diablo 2 as an example. I hope you enjoy it, and if you have any suggestions, feel free to send a message or comment here! submitted by /u/moisesdepaulodias [link] [comments]  ( 9 min )

Good Evening, I and some fellow students are taking a SE class and are looking for relevant information regarding esrb ratings and games for a research project. Does anyone know of any data pertaining to relevant esrb info that we can access? We have a few sources and are waiting to hear back from esrb themselves. Would anyone know if they permit web scraping or if there is a csv containing relevant information, or even perhaps an api we could use? Any information would help and thank you all for taking the time to read this. Thanks in advance! submitted by /u/GOD_LIKE_WOW [link] [comments]  ( 9 min )

I'm currently working on a project to give a quick summary of long articles/conversations. I'm running llama-2-7b-chat-hf with 4bit quantization on a g5.2xlarge instance on sagemaker. The method I'm using is map_reduce (option 2)from this webpage https://python.langchain.com/docs/use_cases/summarization) Of everything I've tried this is the only one that's been able to do decent summaries in a reasonable amount of time. However with really long articles (10,000+ words) it takes ~6 minutes before giving an output. I tried running this same thing on a g5.12xlarge instance which has 4 A10G gpus but it hasn't reduced the time by any noticeable amount. Is there anything else I could be doing to speed this up? submitted by /u/Able_Body_9654 [link] [comments]  ( 9 min )

so as the question states, i want to use llama2 to generate an answer for the question based on the context (or the article for more precision), no finetuning is needed, just want to predict the answer, but i can't find what's the right prompt i should use to get a well structured answer. my dataset contains two columns, one for articles, and the other column is for the question, Example: context: article talking about world war 2. question : based on the text, describe how the ww2 had started, and what were the political effects on Europe? submitted by /u/kaoutar- [link] [comments]  ( 9 min )

There's a new open-access foundational audio model in town! Standing on the shoulders of TorToiSe TTS - XTTS allows cross-language and multi-lingual speech generation with just 3 lines of code 🐸 Key facts about the model: 1. Supports 13 languages. 2. Voice cloning with just a 3-second audio clip. 3. Emotion and style transfer by cloning. 4. Cross-language voice cloning. Try it out on HF Hub: https://huggingface.co/spaces/coqui/xtts submitted by /u/vaibhavs10 [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2309.07062 Abstract: We explore the novel application of Large Language Models to code optimization. We present a 7B-parameter transformer model trained from scratch to optimize LLVM assembly for code size. The model takes as input unoptimized assembly and outputs a list of compiler options to best optimize the program. Crucially, during training, we ask the model to predict the instruction counts before and after optimization, and the optimized code itself. These auxiliary learning tasks significantly improve the optimization performance of the model and improve the model's depth of understanding. We evaluate on a large suite of test programs. Our approach achieves a 3.0% improvement in reducing instruction counts over the compiler, outperforming two state-of-the-art baselines that require thousands of compilations. Furthermore, the model shows surprisingly strong code reasoning abilities, generating compilable code 91% of the time and perfectly emulating the output of the compiler 70% of the time. https://preview.redd.it/f9c7kh7bd9ob1.jpg?width=1530&format=pjpg&auto=webp&s=287fffa714936da9b9a5141b7e01609942416156 https://preview.redd.it/z4a0ce7bd9ob1.jpg?width=1537&format=pjpg&auto=webp&s=e6275b2b53fa6f431b87940784629b3270c656f9 https://preview.redd.it/89toie7bd9ob1.jpg?width=750&format=pjpg&auto=webp&s=9a71bdb2eeeff52b2f8bbb3cf2b678debcd4a060 https://preview.redd.it/0krmqd7bd9ob1.jpg?width=1536&format=pjpg&auto=webp&s=ba3fade0883ee621b185fabc67839db42ea74a53 https://preview.redd.it/8nz00i7bd9ob1.jpg?width=1198&format=pjpg&auto=webp&s=6ddbddf68311f576fbf3c52a47381316feace8c9 ​ ​ submitted by /u/Singularian2501 [link] [comments]  ( 9 min )

Hi everyone! I'm still experiencing with my own implementation of rag, and I deployed my custom chunking function (honestly don't like the methods on LangChain) . Anyway, I'm searching for alternative methods, algoritms (NLP or not) and models... There are lots of info and different implementation on RAG, but as I can see noone put much effort to augment chunking quality. Also, there are other approach than this one I'm currently using? bi-encoder (instructor) - > cross-encoder (reranking) - > LLM Can someone share some resources, repo, lib or existing implementation of different chunking methods? (or simply discuss here some idea, though or approach) Thanks in advance for you time!! submitted by /u/Distinct-Target7503 [link] [comments]  ( 9 min )

The problem is obtained from Chapter 3 in Wright, Stephen J and Benjamin Recht (2022). Optimization for data analysis. Cambridge University Press I am solving the problem I attach and I have a doubt in section (f). I have solved all the sections (a)-(e). In section (e) I have obtained that I need $$ k \geq \frac{\lambda_{\text{max}}\left(\frac{2}{N} A^TA + 2\mu I\right)}{\lambda_{\text{min}}\left(\frac{2}{N} A^TA + 2\mu I\right)}log((f(x^0)-f(x_\mu)/\epsilon). $$ However in section (f) asks for a tight upper bound but I only can think about the following bound: $f(\hat{x}) \leq f_\mu(x_\mu) + ||\hat{x}||^2+ \epsilon$, which is very simple. Do you think that I can obtain the result in (e) to obtain another bond, or what would you do? submitted by /u/ItsGauss [link] [comments]  ( 9 min )

Need help on how to get started with implementing a research paper. I'm implementing the Imagenet classification task paper for my final year undergrad mini-project. Any advice is appreciated on how to get started? I have mid-level machine learning knowledge and am ready to pick the required concepts on the go. Please help. Thank you :) Link: https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf submitted by /u/DrBeans0 [link] [comments]  ( 9 min )

Hi, I'm trying to figure out what is the best way to use LLMs to analyse a very old software entirely written in C. I've tried to to some basic prompts with ChatGPT and it seems to recognise the language. The situation is that I've many .c files with thousands of lines and with a lot of redundant code. Moreover, since there are a lot of data structure with variables names not easily understandable, I need to provide some context to allow the model to trying to understand what the code does. My worry is that providing all the needed info + the file itself (even considering 1 file at a time) I could consume all the model context and therefore not leave room for generating anything of value. Has anyone had the opportunity to face similar problems? Ideas? Cheers Alexio submitted by /u/Alexioc [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2306.04640 GitHub: https://github.com/ibm/moduleformer (under Apache 2.0) Twitter thread: https://twitter.com/Yikang_Shen/status/1702041129267388678 Abstract: Large Language Models (LLMs) have achieved remarkable results. However, existing models are expensive to train and deploy, and it is also difficult to expand their knowledge beyond pre-training data without forgetting previous knowledge. This paper proposes a new neural network architecture, ModuleFormer, that leverages modularity to improve the efficiency and flexibility of large language models. ModuleFormer is based on the Sparse Mixture of Experts (SMoE). Unlike the previous SMoE-based modular language model, which requires domain-labeled data to learn domain-specific experts, ModuleFormer can i…  ( 10 min )

Hey there, I wasn't doing any ml in some time and forgot basics. I was thinking that you may help me. So I trained svc model on small dataset (around 1400 unical records). I have 13 classes, which are badly distributed in the training set and inbalanced. 3 classes took around 80% of all. What the business wants is keep the 3 classes and categorize the rest as "Other" class. In the future they may be able to generate more training data for the remaining labels. How should I do it? I know I can assign everytning to "Other" class, before training with simple if then formula, but dont know if this is the right approach. Any sugestions? I know this may be some case of 1 vs all case, but don't know which exactly. Thanks in advance for any help. submitted by /u/th00masml [link] [comments]  ( 9 min )

Hey everyone! 👋 I’ve been on a bit of a deep-dive lately, trying to catch up on all the awesome stuff that’s been happening in the ML space. It got me wondering, from 2020 to 2023, what have been the absolute must-read papers that shook the foundations and got everyone talking? Whether it’s something that reinvented the wheel in your specific niche or just made waves industry-wide, I wanna hear about it! I’m curious to see how different the responses will be, and hey, this might even become a go-to list for anyone looking to get the lowdown on the hottest trends and discoveries of the past few years. Can’t wait to hear your thoughts! tl;dr I decided to aggregate your best suggestions into categories for anyone interested in reading them without searching through the whole comment se…  ( 10 min )

Hi everyone! I was really excited that Mojo became publicly available and thinking which project can I implement to learn Mojo concepts. Since I have already ported llama2.c to pure Python, I decided why not try to port llama2.py to Mojo now.. And here is what I got First round of llama2.c vs llama2.🔥 battle. Mojo demonstrated 20% better performance than C in a single threaded execution of llama2 inference and 250x times better performance than Python https://i.redd.it/0gcwwfc2r7ob1.gif For reference Mojo is using SIMD vectorization, that's why it's performing great for matmul operations. In the other hand, it turned out that gcc also aggressively optimizes all for loops it can find, so I suggest this benchmark is pretty fair. ​ Mojo natively supports SIMD vectorizations out of the box gcc aggressively vectorizing loops ​ After that I decided to compare both solutions in multi-threaded (parallelized) mode, and now `llama2.c` strike back with help of OMP demonstrating 20% better performance than Mojo ​ https://i.redd.it/gwymffods7ob1.gif I hope this post will be useful for all Machine Learning engineers/enthusiasts/students out there, ensuring we're up-to-date with Modular/Mojo's game-changing AI tech developments. Stay informed and ahead of the curve! Links llama2.🔥: https://github.com/tairov/llama2.mojo llama2.python: https://github.com/tairov/llama2.py llama2.c: https://github.com/karpathy/llama2.c Modular_AI repost in Twitter: https://twitter.com/tairov/status/1701345271752343900 feel the magic on HF: https://huggingface.co/spaces/radames/Gradio-llama2.mojo submitted by /u/Albatross9855 [link] [comments]  ( 9 min )

Hello everyone, I'm currently at a crossroads with a decision that I believe many in this community might have faced or will face at some point: Should I use cloud-based GPU instances like AWS's p3.2xlarge EC2 (with Tesla V100) or invest in building a high-performance rig at home with multiple RTX 4090s for training a large language model? Context: I run a startup and we're currently fine-tuning an open source LLM, and the computational demands are of course high. We want to make an informed choice between using AWS's offerings or setting up a high-performance system at home to start. Cloud Option: AWS p3.2xlarge EC2 Cost: Approximately $3.06 per hour. Specifications: One Tesla V100 GPU, 8 vCPUs, 61 GiB RAM. Pros: Scalability, reliability, specialized software optimizations. Cons: Recurring costs, potential limitations on customization. Home Rig Option: Multiple RTX 4090s Cost: Around $1,600 for each 4090, but I'd own them. Specifications: Even higher TFLOPs than a V100, and memory isn't a constraint (24GB per card). Pros: One-time investment, flexibility, potentially higher raw computational power. Cons: Need to handle cooling, power, and system integration myself I'd love to hear your thoughts, experiences, and recommendations. Here are some specific questions: Performance: How many RTX 4090s would roughly equal the computational power of an AWS p3.2xlarge instance for ML tasks? Cost-Effectiveness: Given that we're a startup with limited resources, does it make more financial sense to invest upfront in hardware? Reliability and Maintenance: For those who have run multi-GPU setups at home, how reliable are they, and what maintenance work is required? Other Considerations: Are there factors I haven't considered that you think are critical? Thank you in advance for sharing your insights! submitted by /u/devolvedai [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2305.16264 GitHub: https://github.com/huggingface/datablations License: All models & code are licensed under Apache 2.0. Filtered datasets are released with the same license as the datasets they stem from. Abstract: The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes. Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero. We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters. Models and datasets from our 400 training runs are freely available at this https URL. ​ https://preview.redd.it/ahzyonnqe7ob1.png?width=1015&format=png&auto=webp&s=0e0cb4051e390ea23440cd61bfc0bbf5fce83bb7 https://preview.redd.it/l6a81onqe7ob1.png?width=1014&format=png&auto=webp&s=a36b74cbb510a1f753ef1b891531bb36ab643246 https://preview.redd.it/yyu8h0oqe7ob1.png?width=1001&format=png&auto=webp&s=047cb2bb1932c6215cea0c30e22fd9bbe60391a8 https://preview.redd.it/xskcytnqe7ob1.png?width=1007&format=png&auto=webp&s=4090e92dd9eacb377840327bb7d0ae69ff752b52 submitted by /u/InterviewIntrepid889 [link] [comments]  ( 9 min )

On my team they are using Gradio for LLM applications, etc. When running multiple instances of Gradio applications on the same server, but on different ports, opening a new session is causing an error and closing the previous session, "error" is written in the chat. The logs I found were like "Invalid HTTP request received" and "max retries exceeded". For me, Gradio is supposed to work as a demonstration and not as a scalable product, but they are using it that way and I thought that the problem could be precisely that. But if not, does anyone have any idea what could be going on? No meu time estão utilizando gradio para aplicações de LLMs, etc. Ao executar múltiplas instâncias de aplicações Gradio no mesmo servidor, mas em portas diferentes, a abertura de uma nova sessão está causando erro e encerrando a sessão anterior, fica "erro" escrito no chat. Os logs que encontrei eram como "Invalid HTTP request received" e "max retries exceeded". Para mim, o Gradio é pra funcionar como demonstração e não em forma de produto escalável, mas estão utilizando assim e pensei que o problema poderia ser justamente isso. Mas caso não, alguém tem alguma ideia do que pode estar acontecendo? submitted by /u/Magic_squirrel_hat [link] [comments]  ( 9 min )

Hi, does anyone have a compiled list of non-open source AI models that can be used for MVP building? submitted by /u/Compound_Group [link] [comments]  ( 9 min )

I had a Claude based LLM analyze the strengths and weaknesses of the MetaGPT, ChatDev, AoT, and brain inspired algorithms papers, seeking ways to combine the strengths of two or more of the methods. "Here are some specific examples of how the methods in the Algorithm of Thoughts (AoT), brain-inspired algorithms, MetaGPT, and ChatDev papers could be combined to improve multi-agent systems: MetaGPT could incorporate longer, more elaborate algorithmic examples from AoT into its prompts to guide the LLM's reasoning process. For example, in solving math word problems, the prompt could provide a 4-5 step worked example walking through unpacking the problem statement, setting up equations, solving, and checking the solution. This mirrors AoT's more extensive algorithm narratives. ChatDev's a…  ( 11 min )

submitted by /u/hauntedhivezzz [link] [comments]  ( 8 min )

I asked ChatGPT to talk with Trump's jury about the 3-hour delay. Not bad. ChatGPT: Ladies and gentlemen of the jury, today, we present a compelling case that the three-hour delay in President Trump's response to the events of January 6th is, without a doubt, overwhelming and irrefutable evidence of his planning and conducting the insurrection that unfolded before our eyes. First, consider the timeline. As the violence escalated, President Trump did not immediately call for an end to the chaos. This delay allowed the mob to wreak havoc, endanger lives, and damage our democracy. A leader's duty is to protect the nation and its citizens, and a three-hour wait to act is inexplicable unless there was intent behind it. Second, let's talk about intent. In criminal law, intent matters. It's the difference between an accident and a crime. A deliberate delay in response to an unfolding insurrection suggests a guilty mind. President Trump had the power and the responsibility to intervene immediately. Yet, he chose not to, allowing the violence to escalate. Furthermore, we must consider his words leading up to and during the events of January 6th. His rhetoric could be interpreted as encouragement for his supporters to take matters into their own hands. This, coupled with the delay, paints a picture of a leader who not only knew what was happening but may have orchestrated it. Ladies and gentlemen, we are not here to pass judgment but to seek justice. The evidence of a three-hour delay in responding to the insurrection, along with the surrounding circumstances, presents a compelling case that President Trump planned and conducted this insurrection. It is our duty to hold individuals accountable for their actions, regardless of their position. submitted by /u/Georgeo57 [link] [comments]  ( 9 min )

How would today's AI perform as a crime investigator or judge? To find out, I asked Bard the following question: The film Social Network suggests that Mark Zuckerberg stole his idea for Facebook from two brothers who hired him to make an app for the idea. How likely is this to be true? Bard seemed informed and sharp: "The film The Social Network is a dramatization of the founding of Facebook, and as such, it takes some liberties with the truth. However, the film does accurately portray the fact that Mark Zuckerberg was sued by the Winklevoss twins, Cameron and Tyler, who claimed that he stole their idea for a social networking website called HarvardConnection. The Winklevoss twins allege that they met with Zuckerberg in December 2003 to discuss their idea for HarvardConnection, and tha…  ( 10 min )

Hi there! I'm participating in a Hackathon at work and am wondering if this community can give me some direction. Every year our company receives mandates / updates from different parties. In turn, our development teams have to parse through heavy documentation to figure out what needs to change in our code. Ingesting the data is what takes the longest. Our goal is to feed the mandates documentation through an AI and have it return what is needed to be changed in our code. For example, something might say field 200 now needs to include a 6 digit date format vs the 4 digit date format we've had in years past. We have secured a license for Azure AI but honestly no idea if that is the right AI to use. I youtubed a bunch of videos on document processing but I'm also not sure if that is what we are trying to do. Any advice on this is much appreciated. submitted by /u/HillyjoKokoMo [link] [comments]  ( 9 min )

I'm looking for a program that will transcribe live audio playing from my computer (windows). Do you know something like that? I've seen Buzz (https://chidiwilliams.github.io/buzz/docs/usage), but it needs an audio loopback driver in order to work, so I wonder if there are others. 🚀 Bonus points if it recognizes different people talking. 🚀 Extra bonus points if it can transcribe multiple languages. submitted by /u/AleHoju [link] [comments]  ( 9 min )

So today I signed up for a debate on ai. Wheather ai is beneficial or dangerous to human beings. I have the freedom to choose any side. This debate will be watched by about 130 people, all cs freshmen (mind you, also my first time speaking in front of this many people). Now, I'm confident I know more about ai than an average person but I need your help in preparing properly. Which side do I take and what are all the points I should keep in mind? It's 4 pm here and the debate is tomorrow. Any help will be appreciated. Thank you. submitted by /u/CalmGuy69 [link] [comments]  ( 9 min )

Artificial intelligence (AI) has the potential to solve the world's problems or destroy humanity. It is being developed by a few hundred individuals in Silicon Valley, and we have little say in its ethics or existence. AI has already demonstrated creative abilities in painting, writing, and music. It is also being used in drug discovery, therapy, dating apps, and misinformation in politics. The rapid adoption of AI raises concerns about job displacement and the potential for catastrophic events. Experts predict a significant chance of AI causing a catastrophe or even wiping out humanity. Source : https://www.vanityfair.com/news/2023/09/artificial-intelligence-industry-future Summarized by Nuse AI submitted by /u/NuseAI [link] [comments]  ( 9 min )

In less than a year since ChatGPT was introduced, it has become the fastest consumer application to register 100 million monthly active users. But how are consumers using other GenAI products apart from ChatGPT? An a16z Consumer report examines the top 50 GenAI web products (based on SimilarWeb data) to find out. To stay on top of the latest advancements in AI, look here first. https://preview.redd.it/f0kh5qag16ob1.png?width=2058&format=png&auto=webp&s=1cab11a31d384c068912c9fca32a000393b795d5 Proprietary models are dominating: 80% of the top 50 GenAI products didn’t exist a year ago—suggesting many of the most compelling consumer experiences are completely novel. Interestingly, 48% of these are bootstrapped—with no outside funding. ChatGPT holds a massive lead: ChatGPT alone accounts for 60% of the entire list's monthly traffic, with roughly 1.6 billion visits and 200 million monthly users as of June 2023. LLM assistants are dominating: LLMs, including Google’s Bard and Quora’s Poe, constitute 68% of total consumer traffic to the top 50. The other categories seeing significant traffic are AI companions and content-generation tools. GenAI marketing is mostly organic: Marketing for most of these products has been reliant on referrals, word of mouth, and other traditional marketing as they enter the market. About 90% of these companies are already monetizing, and most do so via a subscription model. GenAI and mobile adaptability: Given the extensive consumer time spent on mobile, an increase in mobile-first GenAI products is expected as the technology evolves. (source) P.S. If you like this type of analysis, sign up for my free newsletter that deciphers the fastest-moving news and research in AI and tech. Professionals from Google, Meta, and OpenAI are already on board. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

Adobe’s Firefly generative AI tools are now widely available.[1] Stability AI, gunning for a hit, launches an AI-powered music generator.[2] Elon Musk warned of civilizational risks posed by artificial intelligence. Sundar Pichai of Google highlighted the technology’s potential to solve health and energy problems. And Mark Zuckerberg of Meta stressed the importance of open and transparent A.I. systems.[3] German military plows millions into AI ‘environment’ for weapons tests that could change combat forever.[4] Amazon launches generative AI to help sellers write product descriptions.[5] Sources: [1] https://www.theverge.com/2023/9/13/23871537/adobe-firefly-generative-ai-model-general-availability-launch-date-price [2] https://techcrunch.com/2023/09/13/stability-ai-gunning-for-a-hit-launches-an-ai-powered-music-generator/ [3] https://www.nytimes.com/2023/09/13/technology/silicon-valley-ai-washington-schumer.html [4] https://www.foxnews.com/world/german-military-plows-millions-ai-environment-weapons-tests-change-combat [5] https://www.aboutamazon.com/news/small-business/amazon-sellers-generative-ai-tool submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

My experience in CS, coding, and programming is very minimal. I understand general concepts but only through the lens of a degree in physics that required I mess around with WolframAlpha sometimes (which I really enjoyed). I've been considering getting a second degree in CS or something related but want to find a clear(ish) path before committing to it. I would love to hear any related thoughts as well! ​ Prompt Engineering seems like a pretty fresh field of study. Is it up and coming as a career path? People that specialize in this, what field(s) did you come from and how would you recommend diving into it? Considering my experience, would I be able to actually find work, freelancing or employed? How on earth do I get started in this world? It seems so insanely big and complicated but I am just fascinated by the idea of using written dialogue to manipulate the output of an LLM! From my very high overview of PE and the recent advances in AI, PE as a field of study and interest is going to expand exponentially, is that accurate? submitted by /u/Top_Room_6714 [link] [comments]  ( 9 min )

Posted by Gabriel Barcik and Duc-Hieu Tran, Research Engineers, Google Research In today's digital age, smartphones and desktop web browsers serve as the primary tools for accessing news and information. However, the proliferation of website clutter — encompassing complex layouts, navigation elements, and extraneous links — significantly impairs both the reading experience and article navigation. This issue is particularly acute for individuals with accessibility requirements. To improve the user experience and make reading more accessible, Android and Chrome users may leverage the Reading Mode feature, which enhances accessibility by processing webpages to allow customizable contrast, adjustable text size, more legible fonts, and to enable text-to-speech utilities. Additionally, An…  ( 93 min )

I'm trying to build a humanoid model and then train it to perform some tasks , I have decided to go with mujoco for the simulation and now I'm wondering if I should use the C++ API or the python one. the python implementation says it uses C API but is it good? Also if it's slower than the c++ one how slow is it? I'll probably have to make something real time and hence can't compromise much on the speed, but if it's only and small amount it's acceptable. would really appreciate some guidance in this matter thank you submitted by /u/rakk109 [link] [comments]  ( 9 min )

Hello, I am looking for environments to test out some ideas I have for algorithmic pricing. By algorithmic pricing environments, I mean there are multiple competing algorithms trying to maximize profits. I can't really find any out of the box implementations. There are trading environments but those are not what I am looking for. Any help would be appreciated, thanks. submitted by /u/Next_Gap8224 [link] [comments]  ( 9 min )

Hi! Recently, I tried to follow fetch_pick_and_place.env in gymnasium_robotics to build a similar environment with Franka. I found that the core of this implementation is to use the mocap to control the end-effector, and then mocap derives joint angles using the built-in inverse kinematics algorithm. For the fetch_pick_and_place.env, mocap does not cause mutations and oscillations in configuration space. However, when I use mocap to control Franka, oscillations in joint space occur frequently, although I've minimized the step size of the mocap to ensure that the movement of the end-effector in Cartesian space is minor. Fetch and Franka are both redundant arms, I don't know why there is such a big difference in mocap performance. Here is the video to illustrate the above phenomenon Franka I've opened issues on mujoco and gymnasium robotics repositories, but it didn't initiate any discussion. Any help would be appreciated! Thanks! ​ submitted by /u/UpperSearch4172 [link] [comments]  ( 9 min )

In first part of this multi-series blog post, you will learn how to create a scalable training pipeline and prepare training data for Comprehend Custom Classification models. We will introduce a custom classifier training pipeline that can be deployed in your AWS account with few clicks.  ( 10 min )

Today, generative AI models cover a variety of tasks from text summarization, Q&A, and image and video generation. To improve the quality of output, approaches like n-short learning, Prompt engineering, Retrieval Augmented Generation (RAG) and fine tuning are used. Fine-tuning allows you to adjust these generative AI models to achieve improved performance on your domain-specific […]  ( 8 min )

This post takes you through the most common challenges that customers face when searching internal documents, and gives you concrete guidance on how AWS services can be used to create a generative AI conversational bot that makes internal information more useful. Unstructured data accounts for 80% of all the data found within organizations, consisting of […]  ( 14 min )

submitted by /u/pablocarrera [link] [comments]  ( 8 min )

Suppose m = pq where p and q are large, distinct primes. In the previous post we said that calculations mod m can often be carried out more efficiently by working mod p and mod q, then combining the results to get back to a result mod m. The Chinese Remainder Theorem assures us that […] Chinese Remainder Theorem synthesis algorithm first appeared on John D. Cook.  ( 5 min )

Suppose m is a large integer that you are able to factor. To keep things simple, suppose m = pq where p and q are distinct primes; everything in this post generalizes easily to the case of m having more than two factors. You can carry out calculations mod m more efficiently by carrying out […] Gaining efficiency by working modulo factors first appeared on John D. Cook.  ( 5 min )

RSA encryption a map from numbers mod n to numbers mod n where n is a public key. A message is represented as an integer m and is encrypted by computing c = me mod n where e is part of the public key. In practice, e is usually 65537 though it does not have […] Group theory and RSA encryption first appeared on John D. Cook.  ( 7 min )

Not all messages encrypted with the RSA algorithm can be decrypted. This post will show why this is possible and why it does not matter in practice. RSA in a nutshell RSA encryption starts by finding two large primes, p and q. These primes are kept secret, but their product n = pq is made public. […] RSA encrypted messages that cannot be decrypted first appeared on John D. Cook.  ( 6 min )

Modern applications heavily rely on robust network infrastructure, requiring continuous innovation. In this evolving landscape, Microsoft is at the forefront, spearheading innovation efforts in networking and strengthening the foundational network infrastructure that underpins the cloud ecosystem. By investing in and enhancing this critical infrastructure, Microsoft not only ensures the resilience and scalability of cloud services […] The post Microsoft at ACM SIGCOMM 2023: Innovating the future of networking appeared first on Microsoft Research.  ( 10 min )

What’s the driving force behind AI’s recent, rapid progress? Research manager Ahmed Awadallah shares his insights on this, the two-stage approach to training large-scale models, and the need for better model evaluation in this episode of the #MSRPodcast. The post AI Frontiers: The future of scale with Ahmed Awadallah and Ashley Llorens appeared first on Microsoft Research.  ( 31 min )

Working as a data scientist is the dream of many IT professionals these days. It is no secret that data science is a skyrocketing field attracting young professionals and inspiring many to switch careers to data science. On one front are young professionals who study their courses in colleges to pursue their dream of becoming… Read More »Are data science certifications the gateway to competitive pay? The post Are data science certifications the gateway to competitive pay? appeared first on Data Science Central.  ( 19 min )

CUPED: Improve Your A/B Testing - Detect Smaller Gains, Utilise Smaller Samples and Make Smarter Decisions! The post CUPED for starters: Enhancing controlled experiments with pre-experiment data appeared first on Data Science Central.  ( 26 min )

Data centers are known for their impact on the environment. They run 24/7 and exude a lot of heat. Massive warehouses full of hot technology require advanced cooling systems or an HVAC system pushed to its limit.  Data center managers and sustainability leaders no longer settle for antiquated techniques. They’re striving to develop greener and… Read More »Searching for sustainability in data center cooling The post Searching for sustainability in data center cooling appeared first on Data Science Central.  ( 20 min )

The best way to model business and consumer dynamics is collaboratively, with stakeholders all in the same virtual room contributing. Of course, this has been happening asynchronously for some time now, but the potential exists for more real-time interaction.  Modelers don’t work in a vacuum, of course. The iterations between a modeler who develops a… Read More »Collaborative visual knowledge graph modeling at the system level The post Collaborative visual knowledge graph modeling at the system level appeared first on Data Science Central.  ( 20 min )

GFN Thursday is downright demonic, as Devil May Cry 5 comes to GeForce NOW. Capcom’s action-packed third-person brawler leads 15 titles joining the GeForce NOW library this week, including Gears Tactics and The Crew Motorfest. It’s also the last week to take on the Ultimate KovaaK’s Challenge. Get on the leaderboard today for a chance Read article >  ( 6 min )

The machine-learning method works on most mobile devices and could be expanded to assess other motor disorders outside of the doctor’s office.  ( 10 min )

Has anyone ever encountered this error while working with the OpenGL library? raise ImportError("Unable to load OpenGL library", *err.args) ImportError: ('Unable to load OpenGL library', "Could not find module 'OSMesa' (or one of its dependencies) I have done pip install opengl and pyopengl, but it doesn't seem to solve the problem. Specifically, I am trying to run this code: https://github.com/brjathu/LART submitted by /u/BigDreamx [link] [comments]  ( 9 min )

Hi all, I'm new to machine learning and have decided to use a combination of Stable baselines3 and Wandb. I'm at the point where I'm running sweeps using Wandb and want to utilize another PC I have laying around to run agents on. What is the best way to get my python code for the agent to run onto the spare PC? I know I can manually load copy the code over, but I'm looking for a more efficient method that will maintain any changes made to the source code. Maybe packaging up the python code within the sweep and having the agent download and execute it? I'm not all too familiar with possibilities and limits of trying to achieve this so any and all input is appreciated. Thanks! submitted by /u/chip_fork [link] [comments]  ( 9 min )

I have this question that I cannot seem to settle in my head. All papers that I read, report the average (std) performance results across each folds when they report F1, Precision, etc.. Somebody that I highly trust in ML (somebody with a PhD in the field) was reporting the results after saving all the predicted labels (y_pred) and actual labels (y_true) to a list and compute the F1 score one time with the pooled predictions. I now am working on a dataset (binary classification) and trying to validate my model using leave-one-subject-out CV (Some people in my dataset have more lines than others). When I take the average of all iterations I get poor results (F1 score= 0.5), but if I pool all the predictions and compute the F1 score at the end, I achieve decent performance (F1 score =0.7). So, in my project, it is in my best interest to use the second approach, and somebody that I trust tells me that it is okay to do this approach. But I cannot seem to find a paper that says that this approach is acceptable or good. What do you guys think and do you have any suggestions? submitted by /u/enthusiastic31 [link] [comments]  ( 9 min )

I'm working on a project that involves analyzing large samples of Java codes. My end goal is to perform classification based on these codes. For this, I've been trying to efficiently encode the full names of the methods (in the package.class.methodname format) in the Java code. Currently, I am experimenting with doc2vec. I'm treating the components of each method's full name (separated by dots) as individual documents. This allows me to produce vectors for each method name, and I evaluate the results by computing the cosine similarity between pairs of similar method names. The results were not good so far. Before moving to doc2vec, I tried using an LLM which gave me good results. However, the inference time was far too long, especially given the scale at which I'm working. I also considered using a Bag of Words model, but quickly realized it wouldn't be effective. Many of the method names in my samples are obfuscated, making this approach unsuitable. The issue I'm facing is that using the direct method names as features is not generalizing well for classification. A slight change in a method's name results in losing that particular feature, making my model fragile. What are some optimal encoding methods for small sentences (around 5-6 words) like these method names, that can scale well? Also, Are there any specialized encoding techniques tailored for software code that I could use for this task? Any suggestions or insights would be really helpful. Thank you! submitted by /u/Practical_Mango_8720 [link] [comments]  ( 9 min )

Paper: https://arxiv.org/abs/2309.06180 Github: https://github.com/vllm-project/vllm Blog: https://vllm.ai/ Abstract: High throughput serving of large language models (LLMs) requires batching sufficiently many requests at a time. However, existing systems struggle because the key-value cache (KV cache) memory for each request is huge and grows and shrinks dynamically. When managed inefficiently, this memory can be significantly wasted by fragmentation and redundant duplication, limiting the batch size. To address this problem, we propose PagedAttention, an attention algorithm inspired by the classical virtual memory and paging techniques in operating systems. On top of it, we build vLLM, an LLM serving system that achieves (1) near-zero waste in KV cache memory and (2) flexible sharing of KV cache within and across requests to further reduce memory usage. Our evaluations show that vLLM improves the throughput of popular LLMs by 2-4× with the same level of latency compared to the state-of-the-art systems, such as FasterTransformer and Orca. The improvement is more pronounced with longer sequences, larger models, and more complex decoding algorithms. https://preview.redd.it/x8w8ckejv2ob1.jpg?width=667&format=pjpg&auto=webp&s=28fae778b67ac28fc72d084f071b12c92cb5ea07 https://preview.redd.it/ctlrqpejv2ob1.jpg?width=1468&format=pjpg&auto=webp&s=31755d169673ee5d30efa3f05bd6cb10813b328d https://preview.redd.it/z5r7knejv2ob1.jpg?width=1504&format=pjpg&auto=webp&s=9ceb5370aa5a7cc0688fe9a3771a0328262c3a01 ​ ​ submitted by /u/Singularian2501 [link] [comments]  ( 9 min )

Problem: Given a database of the most viral tweets (of a certain shared category), I am hoping to use LLM's to generate further viral tweets. Currently I am seeing this as a synthetic data generation problem: two approaches I am thinking of is 1) grounding (using viral examples to guide the prompt) and 2) filtering (finetuning an LLM to predict virality and filtering for the most viral generations) I want to ensure that the output retains the "viral" structure/style and is diverse/new (i.e no copies). Any general directions or references are appreciated submitted by /u/greatSWE [link] [comments]  ( 9 min )

I'm currently doing Integrated MSc in Mathematics (in India) and until now I've done a remote research intern in a French university and I'll do a research intern at a French research laboratory (INRIA-LORIA) next year, I want a job (in India) at the R&D sector (Data Scientist or ML Engineering anything would do) Idk if research interns are as valuable as Industry internships when it comes to R&D? Basically after my masters, I'll probably have only these two internships to show as a work experience, Probably won't be able to get an internship in an IT company unless i opt for remote work (which is also not guaranteed atm). Mu question, generally is Msc + 2 research internships + 1or 2 publications good enough to secure a decent job in R&D? submitted by /u/Emotional-Zebra5359 [link] [comments]  ( 9 min )

Hello!! I have a BSc in Mathematics and currently I'm going to start a MSc in FinTech ( it has 3 courses out of 9 that have to do with ML, NN and many more ). Since I am really fascinated about programming ( didn't like it when I was at Maths department because of teachers and I am starting learning on my own through courses and we will have many programming languages in my masters degree ex. R,Python, SQL and others) and I would start as a data scientist at the beginning, could I through years of experience later ( ideally 1-2 years later, provided that I have a solid and good programming skills and projects) become a ML engineer? ( Now of course I can't become one because I know that it is difficult since I am competing with people that have CS degrees). submitted by /u/math-is-cool-62 [link] [comments]  ( 9 min )

Hi there! I bring 2 years of experience from a European investment fund and a solid idea for a B2B SaaS solution targeting private equity investment funds. This market is notoriously challenging to penetrate without insider knowledge. The timing couldn't be better. Similar kind of software is currently sold €20k per user by a semi-monopolistic boomer company that is ready to be disrupted. Offer: - Equity shared equally. - Ready to quit my job and go full-time on it, if I find the right co-founder. Ideal Partner: Seeking someone proficient in SQL for handling large datasets and able to integrate OpenAI's API within such datasets (technical AI skills required). While I'm based in Berlin and prefer a European co-founder, it's not mandatory, but meeting in person is a must before we commit. Don't be afraid to DM me if intrigued! Together, we can make it happen. Let's revolutionize the sector! submitted by /u/Sudden_Possible489 [link] [comments]  ( 9 min )

İ am lookong for the pdfs about machine learning, maths for ML, ml projects. İs there any sites i can find pdf like that? submitted by /u/Necessary-Car-5080 [link] [comments]  ( 9 min )

Hey, I've been using TF pretty much my whole deep learning career starting in 2017. I've also used it on Windows the entire time. This was never a major issue. Now when I tried (somewhat belatedly) upgrading from 2.10 to 2.13, I see the GPU isnt being utilized and upon further digging see that they dropped Windows GPU support after 2.10: "Caution: TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Starting with TensorFlow 2.11, you will need to install TensorFlow in WSL2, or install tensorflow or tensorflow-cpu and, optionally, try the TensorFlow-DirectML-Plugin" This is really upsetting! Most of the ML developers I know actually use Windows machines since we develop locally and only switch to Linux for deployment. I know WSL is an option, but it (1) can only use 50% RAM (2) doesnt use the native file system. I feel very betrayed. After sticking with, and even advocating for Tensorflow when everyone was (and still is) switching to PyTorch, TF dropped me! This is probably the final nail in the coffin for me. I will be switching to PyTorch as soon as I can :-( -Disgruntled user submitted by /u/rsandler [link] [comments]  ( 9 min )

https://research.sc/participant/login/dynamic/A1D66883-6E8F-409B-8EF9-AC989A76C7E9 Psychology researchers at Swansea University are carrying out an original study to see whether artificial intelligence is able to generate infant faces between the ages of 0 and 7 years. AI generated pictures will be presented alongside real faces from an existing face database, with a sliding scale underneath that you will use to roughly estimate the age of the child’s face. The experiment should only take between 15 and 20 minutes Thank you for your time. submitted by /u/Logipsychlical [link] [comments]  ( 9 min )

So I'll be presenting a talk on Data pre-processing in deep learning in my city's Keras Community Day, and I am still thinking about all the content I want to present there. What I want from this session is to present different ways of preprocessing the data for a deep learning model. I want to show different types of implementations, how those affect the final trained model, when to use which type of data preprocessing and things similar to this. It would be great if you can suggest me some topic, notebooks or datasets for the same. All the notebooks that show good implementation and affect of data preprocessing are absolutely welcome. Also, as this is **Keras** Community Day, I'll have to include more about data preprocessing using Keras and less about other libraries. Also, if you could help me with this: I am confused between showing preprocessing using layers or doing the preprocessing without layers. I know this sounds vague, but if you have any idea about this, let me know. Thank you for reading! submitted by /u/inclinedadarsh [link] [comments]  ( 9 min )

I'm currently in my first year of MSc. in Engineering Mathematics and Computational Science. First Study Period (Currently) I have nonlinear optimization and High-performance computing. The track I want to choose is a mix between Machine Learning and Big Data. I can select 2 courses for Study Period 2. Here are the potential courses to select from: Game Theory and Rationality Large-Scale Optimization Advanced Probabilistic machine learning Basic Stochastic Processes Options and Mathematics (Options trading) Foundations of Probability Theory ​ I need to select 4 potential courses and rank them from high preference to low preference. In case I don't get one of them, the other will be preferred. Please Machine Learning Reddit Gods, Help me. submitted by /u/AdMaster9439 [link] [comments]  ( 9 min )

Hi r/MachineLearning, TL;DR: Run AI apps on pay-per-second cloud GPUs that hot reload with your code changes. Documentation: https://docs.beam.cloud I’m Eli, and my co-founder and I built Beam to run workloads on serverless cloud GPUs with hot reloading, autoscaling, and (of course) fast cold start. You don’t need Docker or AWS to use it, and everyone who signs up gets 10 hours of free GPU credit to try it out. Here a few examples of things you can run on Beam: Fine-tune a LLaMA LLM Transcribe videos with Whisper Train a custom stable diffusion model Beam is built for a fast developer experience. We’ve felt that using Docker and AWS directly is too slow for iterative development. You’ll often find yourself making changes to your code and waiting 10 minutes for a new image to bu…  ( 10 min )

Hey r/machinelearning! I'm thrilled and anxious to share an early version of the MLflow Plugin Manager. It's designed to simplify your mlflow installation, allowing you to install, update, and uninstall MLflow plugins directly from the web interface. Think of it as the "wbond's package manager for sublime", but tailored for MLflow! 📽️ https://i.redd.it/9gj8vqcz01ob1.gif ​ Yes, it's in its infant stages and doesn't boast of a fancy UI yet, but I'm eager to get your feedback! 🔍 What are your first impressions? Is this a good idea? 💡 Any features you'd love to see? 🌐 Ideas on promoting or expanding its reach? I built this to bring a bit more ease to our community. Can't wait to hear your thoughts and where we can take this next! Repo: https://github.com/thijsdezoete/mlflow-plugin-manager/ submitted by /u/jessepnk [link] [comments]  ( 9 min )

Hi all, Someone asked me today "why are LLMs still non deterministic in their output when temperature is set to 0. Assume fixed model between runs on the same machine" I was like WTF are you saying - the randomness in LLM comes from temperature - chat gpt etc.. might have other randomness in the process but we don't have exact info on this. What I know is that in a standard transformers architecture, temperature is the only parameter that can enduce non deterministic behaviour at inference time. He was convinced that there was more to it "i spoke about this to other LLM experts and they also are not sure" I'm like wtf - I start looking up online and do find some people who claim that temperature is not the only thing that influences stochasticity during inference, but I can't find an answer as to what it is exactly. Anyone has a clue of what I am missing here? Thanks! submitted by /u/WagnerianJLC [link] [comments]  ( 9 min )

​ A composite of specialized Tsetlin machines that enables plug-and-play collaboration. I have a love-and-hate relationship with CIFAR-10/100. I love the datasets for the challenge. On the other hand, they are two datasets where Tsetlin machines have struggled with getting state-of-the-art performance. (The Tsetlin machine is a low-energy logic-based alternative to deep learning that has done well on MNIST, Fashion-MNIST, CIFAR-2, and various NLP tasks.) I have been working for some time now on figuring out a solution, and this summer, I finally had a breakthrough: a new architecture that allows multiple Tsetlin machines to collaborate in a plug-and-play manner, forming a Tsetlin machine composite. The collaboration relies on a Tsetlin machine's ability to specialize during learning and…  ( 10 min )

Hello everyone! Have you ever thought about how to create an object detection system using YOLO that works with any game? If you're interested, I've created a tutorial with all the steps to develop this system. I'll leave a link to the video where I demonstrate the process step by step using the game Diablo 2 as an example. I hope you enjoy it, and if you have any suggestions, feel free to send a message or comment here! link to the tutorial: https://www.linkedin.com/posts/moisesdias_english-version-below-tutorial-crie-activity-7107686497885011969-ZLVW/ submitted by /u/moisesdepaulodias [link] [comments]  ( 9 min )

A curated reading list for the adversarial perspective in deep reinforcement learning. https://github.com/EzgiKorkmaz/adversarial-reinforcement-learning submitted by /u/ml_dnn [link] [comments]  ( 9 min )

Can a single layer neural network solve the XOR problem? Most answers say no, but with this one weird trick the answer is yes! And we don't even need a bias! The trick is to multiply the outputs of a single (2,2) linear layer. Here is how: class XorSolver(nn.Module): def __init__(self, *args, **kwargs) -> None: super().__init__(*args, **kwargs) self.layer = nn.Linear(2, 2, bias=False) # we don't even need a bias! def forward(self, x: torch.Tensor) -> torch.Tensor: z = self.layer.forward(x) y = z[:, 0] * z[:, 1] return y This is the loss and model output after 5000 epochs: loss: 6.516383166399464e-08 Input: [[0. 0.] [0. 1.] [1. 0.] [1. 1.]] Model output: [0. 1. 1. 0.] Expected output: [0. 1. 1. 0.] Layer weight: [[-1.248097 1.2202195 ] [-0.80121976 0.81952316]] The full implementation with training and inference - around 50 lines of code - can be found on GitHub. Why it works? Basically the model simulates a more sophisticated neuron which allows more interactions between the inputs. By multiplying the outputs of two neurons, we introduce a form of non-linearity that allows us to separate data that are not linearly separable, like in the XOR problem: f(x1, x2, w1, w2, w3, w4) = (x1 * w1 + x2 * w2) * (x1 * w3 + x2 * w4) w1, w2 are learnable parameters of the first neuron w3, w4 are learnable parameters of the second neuron x1, x2 are inputs to the model Related studies Solving XOR with a single Perceptron Artificial Neural Networks With Adaptive Polynomial Activation Function Single Cortical Neurons as Deep Artificial Neural Networks Dendritic action potentials and computation in human layer 2/3 cortical neurons submitted by /u/tecbar [link] [comments]  ( 9 min )

Hello guys. As I plan on soing a scientific research project, I would need some cloud compute. Say for a month of usage(can I rent for month?). What are some popular options? I am looking at something with 48gb vram pooled,mybe a600 or a100 and some decent cpu, and 2tb space. submitted by /u/Outrageous_Ad1452 [link] [comments]  ( 9 min )

Currently I'm training medium (1B-3B) sized audio models. I have several different architectures in mind. Obviously I don't want to train the full-sized models and then compare them, thats a waste of money. So I'm thinking of training smaller versions (~100M) and then comparing those instead. My question is there some sort of best practice for this? Some smaller multiple of your full model size where it is best to compare? Thanks. submitted by /u/ginger_turmeric [link] [comments]  ( 9 min )

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Hi, I would love to create a small group of people who work together in AI. The project would be to create an AI that can infer new novel knowledge from existing datasets, as opposed to be being limited by operating within the training data. Specifically to be used in the quest to learn more about the universe. So I am looking for a team of likeminded individuals who want to grow in the field of AI. I'd love to setup a discord, subreddit and github profile to showcase our work. My introduction question is: How do we get AI's to expand upon current knowledge instead of just serving from the knowledge itself. Anyone interested in joining me in this? submitted by /u/Miserable-Cobbler-16 [link] [comments]  ( 9 min )

Eight big names in tech, including Nvidia, Palantir, and Adobe, have agreed to red team their AI applications before they're released and prioritize research that will make their systems more trustworthy. The White House has secured voluntary commitments from Adobe, Cohere, IBM, Nvidia, Palantir, Salesforce, Scale AI, and Stability AI to develop machine-learning software and models in a safe, secure, and trustworthy way. The commitments only cover future generative AI models. Each of the corporations has promised to submit their software to internal and external audits, where independent experts can attack the models to see how they can be misused. The organizations agreed to safeguard their intellectual property and make sure things like the weights of their proprietary neural networks don't leak, while giving users a way to easily report vulnerabilities or bugs. All eight companies agreed to focus on research to investigate societal and civil risks AI might pose if they lead to discriminatory decision-making or have weaknesses in data privacy. The US government wants Big Tech to develop watermarking techniques that can identify AI-generated content. The US has asked the corporations to commit to building models for good, such as fighting climate change or improving healthcare. Source : https://www.theregister.com/2023/09/12/nvidia_adobe_palantir_ai_safety/ submitted by /u/NuseAI [link] [comments]  ( 9 min )

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I’ve been using the paid version of GPT for a while but I think it’s time to move on. For $20 a month i would at least like for there to be an option to input an image, but I would also maybe pay a bit more than that per month for a suite of tools or something if it could also do image generation in addition to just text. I’m sorry if it seems like I should be able to understand anything better - please note I am disabled, my use of the tools is personal (creative and household) and not professional, and I’m doing my best by asking here. I do not mean to bother anyone with my own ignorance. Thank you. submitted by /u/CaveLady3000 [link] [comments]  ( 9 min )

Project Gutenberg and Microsoft have created thousands of free audiobooks that use neural text-to-speech technology to generate the voices.[1] A group of U.S. authors, including Pulitzer Prize winner Michael Chabon, has sued OpenAI in federal court in San Francisco, accusing the Microsoft-backed program of misusing their writing to train its popular artificial intelligence-powered chatbot ChatGPT.[2] Numenta launches brain-based NuPIC to make AI processing up to 100 times more efficient.[3] Adept AI Labs released Persimmon-8B. Persimmon-8B is an open-source, fully permissively licensed model in the 8B class. This model holds immense potential for a wide array of applications, aiming to assist users in various computer-related tasks.[4] Sources: [1] https://www.zdnet.com/article/heres-how-to-access-thousands-of-free-audiobooks-thanks-to-microsoft-ai-and-project-gutenberg/ [2] https://www.reuters.com/technology/more-writers-sue-openai-copyright-infringement-over-ai-training-2023-09-11/ [3] https://venturebeat.com/ai/numenta-launches-brain-based-nupic-to-make-ai-processing-up-to-100-times-more-efficient/ [4] https://www.marktechpost.com/2023/09/09/adept-ai-labs-open-sources-persimmon-8b-a-powerful-fully-permissively-licensed-language-model-with/ submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )

I see a ton of AI image tools out there. Some let you upload image files and modulate/modify them in some way. I am wondering if a tool exists that will take a real life product image and make it appear more like a render/computer generated image. Essential I would love to be able to take a pic of a product and use automatic smoothing and AI simulated rendering to output a clean image that looks like a 3d render. This would be used as a product image for an e-commerce website. submitted by /u/ElonMusk0fficial [link] [comments]  ( 9 min )

Alibaba Cloud has open sourced two of its generative AI models, Qwen-7B and Qwen-7B-Chat, for commercial and research use. The models' codes and documentation will be accessible through Alibaba Cloud's AI model repository ModelScope and the US collaborative AI platform Hugging Face. Companies with fewer than 100 million monthly active users can use the models for commercial purposes free of charge, while those with more users will need to request a license. Alibaba aims to democratize AI technology and support LLM start-ups. Alibaba Cloud's ModelScope platform currently features over 1,000 ready-to-use AI models contributed by 20 leading AI institutions. Source : https://www.scmp.com/tech/big-tech/article/3229907/alibaba-cloud-open-sources-its-two-generative-ai-models-based-chatgpt-style-tongyi-qianwen submitted by /u/NuseAI [link] [comments]  ( 9 min )

I have a text script that I want to turn into a video. For the sake of context, the video is on balancing a person’s daily activities. I’m getting tired trying to find matching stock footages for the videos. I was wondering if there is a way to do this using AI tools? Synthesia won’t do because it looks like a video narration, more than a video essay. Any suggestions would help. Thanks in advanced! submitted by /u/Entaro2109 [link] [comments]  ( 9 min )

Hi everyone, I'm hunting for a HumanEval Benchmark for Google's Codey model and am having a tough time hunting it down. Can anyone point me to an Arxiv paper or a coding leaderboard that includes Codey? Thanks! submitted by /u/Iamreason [link] [comments]  ( 9 min )

But product & tech teams succumb to the pressure and move on to developing proof of concepts & even launch products that fail to achieve ROI Why? 1- use cases are not well defined 2- not enough data or right data strategy 3- data and model architecture not founded well I love Apple’s approach to AI, they shy away from the hype and focus on the fundamentals. First the customer, product, then the tech that will add the value the customer needs. What do you think are the top reasons generative AI applications succeed? View Poll submitted by /u/AILaunchpad [link] [comments]  ( 9 min )

I have an open book exam that has a lot of information that I will need to physically search through. Is there a way i can load all he PDFS and create a customised chatgbt style search, so i can easily look through all the information and research i have? submitted by /u/yellowmushroom22 [link] [comments]  ( 9 min )

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I wanna develop small scale personal AI apps for each my friends and AI said i should learn Python, TensorFlow Lite, SQLite, GCP, Pandas, Scikit Learn and Keras. How right is this? submitted by /u/Leading-Ad2278 [link] [comments]  ( 9 min )

Tests carried out by Logically confirm these platforms accept 85% of prompts tailored for election manipulation. submitted by /u/Asleep-Television-24 [link] [comments]  ( 9 min )

AI Chatbots, such as OpenAI's ChatGPT, can create incredibly cost-friendly software in record time, reveals a new study. To stay on top of the latest advancements in AI, look here first. https://preview.redd.it/rxpr6db3aynb1.png?width=1300&format=png&auto=webp&s=721ff5d8f9d25b5e48fa26e2b335c1d20620a83a The AI Tech Company Experiment Brown University and several Chinese University researchers put ChatGPT-powered AI bots to the test by making them run a hypothetical software development company, ChatDev. The AI chatbots were given specific roles and allocated respective stages based on the traditional waterfall model encompassing designing, coding, testing, and documenting. AI 'employees' functioned with minimal human input to complete their parts of the software development process. Impressive Results Assigning 70 tasks to ChatDev led to the completion of the entire software development process in under seven minutes at a cost of less than one dollar on average. A stunning 86.66% of the generated software systems performed flawlessly. Despite some language model errors and biases, the study demonstrates AI's immense potential in automating tasks - a boon, especially to junior programmers around the world. Broader Implications Powerfully generative AI technologies like ChatGPT can perform specific job functions, saving time, and boosting productivity in several industries. While coders find such tools beneficial, it's also critical to note that limitations and biases do exist in AI models which could potentially affect the software creation process. (source) P.S. If you like this kind of analysis, I write a free newsletter that tracks the most vital news and research in AI. Professionals from Google, Meta, and OpenAI are already reading it. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

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>Sailea is a student run non-profit that does not charge for any of its services 🌟 Join SAILea’s Free Webinar with Dr. Richard Marks! 🌟 🗓️ Date: September 23rd, 2023 ⏰ Time: 3:00-4:00PM EST Don't miss an exclusive opportunity to learn from an AI expert! Join us for a free webinar featuring Dr. Richard Marks, a renowned CS and Data Science professor at UNC-Chapel Hill University with a remarkable journey – from Google to PlayStation, and the mind behind EyeToy and PlayStation Move. 🚀 What to Expect: 🔹 Deep insights into tech innovation. 🔹 Career advice. 🔹 Live Q&A with Dr. Richard Marks. Reserve your spot now: sailea.org/events 🔥 Don’t miss this opportunity! Register today!🔥 submitted by /u/Envoy-Insc [link] [comments]  ( 9 min )

I was thinking of some major challenges in modeling a generic real-world environment. Some of them are: adaptive agents, uncertain intentions, and lack of common knowledge. However, most of the papers I see on RL make some assumption or other violating one or more of these, like considering simple agents, assuming known knowledge about others' intentions, and considering that the models of other agents are known when irl an agent hardly has a model of other agents it interacts with apriori. Consider an airport scenario where agents are trying to get into respective planes within a given time, and the gates to each plane allow one person at a time. Looking at the scenario from the view of a single agent, they know what they want, but they can't really make any assumptions about the intentions, strategy, and complexity of other agents beforehand. These other agents can be neutral or adversarial (competing for getting in the same plane) from the agent's viewpoint. All they can see is a restricted view of the motions of some of the other agents. What would you say could model and provide a solution in such a scenario? It is to be noted that other agents can change their strategies based on actions taken by you till now, and so can you. Due to having incomplete information, I fail to see the notion of an equilibrium, and the agents needn't be fully rational as well. submitted by /u/Quirky_Concoction [link] [comments]  ( 9 min )

https://github.com/lowrollr/turbozero I've recently been working on TurboZero, a vectorized implementation of AlphaZero where model inference, search (MCTS), and environment simulation all run in parallel on the GPU. I've also implemented a self-contained training/evaluation pipeline, along with a few environments. I've written a wiki and a starter notebook for those who want to dig deeper. This project is similar to DeepMind's mctx, but supports MCTS subtree persistence (unnecessary for MuZero, which is what mctx was mainly built to support), is written with PyTorch rather than JAX, and can also stand on its own and train models end-to-end. I hope to continue to expand and improve upon this as time allows, and I hope someone here might find it useful or interesting! This is my first major open-source project of any real substance and I still don't have tons of experience with RL, so any feedback/advice is greatly appreciated. submitted by /u/lowrollr [link] [comments]  ( 9 min )

Although computer scientists may initially treat data bias and error as a nuisance, researchers argue it’s a hidden treasure trove for reflecting societal values.  ( 10 min )

Researchers use synthetic data to improve a model’s ability to grasp conceptual information, which could enhance automatic captioning and question-answering systems.  ( 10 min )

When he isn’t investigating human motor control, the graduate student gives back by volunteering with programs that helped him grow as a researcher.  ( 10 min )

Searching for insights in a repository of free-form text documents can be like finding a needle in a haystack. A traditional approach might be to use word counting or other basic analysis to parse documents, but with the power of Amazon AI and machine learning (ML) tools, we can gather deeper understanding of the content. […]  ( 8 min )

In this issue: Efficient polyglot analytics on semantic data aids query performance; generative retrieval for conversational question answering improves dialogue-based interfaces; a new tool uses ML to address capacity degradation in lithium-ion batteries. The post Research Focus: Week of September 11, 2023 appeared first on Microsoft Research.  ( 9 min )

The new #MSRPodcast series “Abstracts” is your source for cutting-edge research in brief. In the first episode, join researchers Ava Amini and Kevin K. Yang to learn about their new paper on using evolutionary-scale protein data to improve protein design. The post Abstracts: September 13, 2023 appeared first on Microsoft Research.  ( 13 min )

Generative AI-based models can not only learn and understand natural languages — they can learn the very language of nature itself, presenting new possibilities for scientific research. Anima Anandkumar, Bren Professor at Caltech and senior director of AI research at NVIDIA, was recently invited to speak at the President’s Council of Advisors on Science and Read article >  ( 5 min )

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We’re growing our presence in Europe with an office in Dublin, Ireland.  ( 2 min )

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So, I've been working on an AI data marketplace platform for a few months now. Users can buy, sell, request, and subscribe to data/datasets (and soon even train their ML/AI models using other users' datasets). One of our key features is the request feature, which allows users to submit data requests for free. These requests include descriptions, required fields, geographical scope, budget etc... Once a request is posted, it's sent to numerous companies, organizations, and data vendors that have the potential to fulfill it. I understand how frustrating the data acquisition process can be, so I designed this platform to be your one-stop shop for all data-related transactions. You no longer have to spend weeks or months dealing with different vendors and companies through slow emails. With our platform, you can request, negotiate, and purchase data all in one place, and it's completely free to post a request, by the way. We've already achieved some successes, and we hope to help more people access the datasets they need. After all, the best AI models are built on diverse and differentiating data. We've had some notable achievements, and we're eager to see if we can fulfill even more interesting dataset requests! submitted by /u/nobilis_rex_ [link] [comments]  ( 9 min )

Morgan Stanley Research has valued Tesla's soon-to-be-released Dojo supercomputer at up to $500 billion, potentially increasing the auto giant's valuation significantly. The financial institution believes Dojo’s applications will go beyond Tesla's Full-Self Driving (FSD) capabilities. To stay on top of the latest advancements in AI, look here first. https://preview.redd.it/yhia3c5f1vnb1.jpg?width=1440&format=pjpg&auto=webp&s=7adf40e7b868a4fbf6eb3d696132652f4f549f23 Morgan Stanley's Bullish Prediction on Dojo Morgan Stanley has suggested that Dojo might not just enhance Tesla's FSD technology, but could find use in other devices that make real-time decisions based on a visual field. Apart from raising Tesla’s valuation, this could potentially open up new markets for the company. Following this, Morgan Stanley has increased its target price for Tesla shares from $250 to $400 each. Dojo Supercomputer Overview Tesla has developed Dojo in-house, diverging from conventional AI accelerators and involving its own computing, networking, IO, and instruction set. At the heart of Dojo is the D1 AI accelerator processor, containing 354 custom CPU cores. Twenty-five D1 chips are combined to create a Dojo training tile, which could expedite Tesla’s move towards earning revenue from vehicle software. Future Plans and Implications Tesla could potentially become an AI-as-a-service provider to automakers in need of FSD capabilities with Dojo. As the development of Dojo continues, Tesla has invested in alternative AI infrastructures, including a cluster of 10,000 of Nvidia's most potent H100 accelerators. (source) P.S. If you like this kind of analysis, I write a free newsletter that explores the latest AI developments. Professionals from Google, Meta, and OpenAI are already reading it. submitted by /u/AIsupercharged [link] [comments]  ( 9 min )

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Somewhere in the world there's a person who is the smartest. Why stop there? There are ten people who are the smartest. And if they are on the Internet, AI will find them. Perhaps not yet. It probably needs to get smarter. Maybe by Gemini. Or GPT-5. But eventually an AI will analyze all of the content on the Internet, and determine from that data who are the ten most intelligent people on the planet, (whose material is online). Of course if AI can determine the top ten, it can certainly determine the top 100, and the top 1,000, and even the top 100,000. I suppose when that happens there will be a lot more human brain power available to solve our problems. Although by then AI will be solving them far better than we could, haha. But think about it for a minute. There are very smart people out there who don't publish in traditional mass media channels. The geniuses among us who don't fit in so well, and are therefore resigned to the margins, remaining unrecognized. Wouldn't it be great if AI discovered them, and gave them the validation they deserve? Wouldn't it be great to find out who they are so that they can better work on whatever. submitted by /u/Georgeo57 [link] [comments]  ( 9 min )

The US sanctions on China's AI and semiconductor industries have failed to limit their growth and development. China is rapidly developing supercomputing capabilities and aims to become the world leader in AI by 2030. Chinese companies like Huawei and SMIC continue to import advanced semiconductor manufacturing equipment and develop their own chips, indicating that the export controls have not been effective. The article explores the trajectory of Chinese domestic semiconductor manufacturing and AI capabilities, as well as the potential impact on companies like Apple, Qualcomm, and MediaTek. It also discusses the potential responses from the US and its allies to counter China's advancements. Source : https://www.semianalysis.com/p/china-ai-and-semiconductors-rise Summarized by Nuse AI submitted by /u/NuseAI [link] [comments]  ( 9 min )

You can install it from the Chrome web store. https://reddit.com/link/16gm8bq/video/yyhx45xjgsnb1/player ​ submitted by /u/MiladMansory [link] [comments]  ( 9 min )